SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Authors: Xuehang Guo
Xingyao Wang
Yangyi Chen
Sha Li
Chi Han
Manling Li
Heng Ji
Venue: ICML 2025 poster (2025)
Keywords: Collaborative Software Engineering
Collaborative Systems
Human-AI Interaction
Agent Evaluation
Benchmark Construction
Large Language Models
Primary area: general_machine_learning->evaluation
TL;DR: We propose SyncMind for evaluating AI agents' out-of-sync recovery in collaborative software engineering based on our benchmark SyncBench, revealing promising capabilities and key limitations in current LLM agents.

Abstract: Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants---whether humans or AI agents---to stay on the same page as their environment evolves. When a collaborator's understanding diverges from the current state---what we term the *out-of-sync* challenge---the collaborator's actions may fail, leading to integration issues. In this work, we introduce **SyncMind**, a framework that systematically defines the *out-of-sync* problem faced by large language model (LLM) agents in collaborative software engineering (CSE). Based on ***SyncMind***, we create **SyncBench**, a benchmark featuring 24,332 instances of agent *out-of-sync* scenarios in real-world CSE derived from 21 popular *GitHub* repositories with executable verification tests. Experiments on ***SyncBench*** uncover critical insights into existing LLM agents' capabilities and limitations. Besides substantial performance gaps among agents (from *Llama-3.1* agents $\leq 3.33\%$ to *Claude-3.5-Sonnet* $\geq 28.18\%$), their consistently low collaboration willingness ($\le 4.86\%$) suggests fundamental limitations of existing LLM in CSE. However, when collaboration occurs, it positively correlates with *out-of-sync* recovery success. Minimal performance differences in agents' resource-aware *out-of-sync* recoveries further reveal their significant lack of resource awareness and adaptability, shedding light on future development of resource-efficient collaborative systems. Our code and data are openly available on our project website: https://xhguo7.github.io/SyncMind/.

SyncMind:
Measuring Agent Out-of-Syne Recovery in Collaborative Software Engincering
Xuehang Guo"
Xingyao Wang
Yangyi Chen
Sha Li Chi Han
Manling Li Heng Ji
Abstract
recoveries further reveal their significant lack of resource awareness and adaptability, shed- ding light on future development of resource- efficient collaborative systems. Our code and data are openly available on our project website:
Software engineering (SE) is increasingly col- laborative, with developers working together on shared complex codebases. Effective col- laboration in shared environments requires participants-whettee humans or Al agents- stay on the same page as their environ- ment evolves. When collaborator's under- standing diverges from the current state-what we term the out-of-sync challenge- the col- laborator's actions may fail, leading to inte- gration issues. In this work, we introduce SyncMind, a framework that systematically de- fines the out-of-syne problem faced by large language model (LLM) agents in collabora- tive software engineering (CSE). Based on SynMind,, we create SyncBench, bench- mark featuring 24,332 instances of agent out- of-sync scenarios real-world CSE derived from 21 popular Gitllub repositories with ex- ecutable verification tests. Experiments on SyncBench uncover critical insights into exist- ing LLM agents' capabilities and limitations. Besides substantial performance gaps among agents (from Llama-3.1 agents < 3.33% to Claude-3.5-Sonnet 2 28.18%), their consis- tently low collaboration willingness (S 4.86%) suggests fundamental limitations of existing LLM in CSE. However, when collaboration oc- curs, it positively correlates with out-of-syne recovery success. Minimal performance dif- ferences in agents' resource-aware out-of-syne
1. Introduction
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Figure 1. The Out-of-Syne Challenge. At T Agent and Hu- man work on respective tasks. During Agent's 's task completion from T: to Te Human updates <repoo> at T3 that Agent is unaware of due to being occupied with its own task. This leads Agent to become out-of-sync
TR as result Sa. Bg.
agents, or saatess-whether involving humans, AI efficiency and capabilities by com- bining complementary strengths. Recent advances have demonstrated impressive capabilities of Al agents in collab- orative tasks (Wang et al., 2024c), from conversational Al as- sistants, like ChatGPT (OpenAI, 2022), Claude (Anthropic, 2023), that effectively assist users in daily problem-solving, to coding agents, like Devin (Cognition AI, 2024), Open- Hands (Wang et al., 2024a), that can actively collaborate with humans on software development.
Collaborative
Univeesiity of Illinois lihana-Chaappainn AAll Hands AI NNottwwester University. Work Work done during intem- ship UTUC. Correspondence to: Xuchang Guo <xue- hangg @illinois.euu>, Xingyao Wang <xingyao@alll @allonansl--e-eeeee xingyao6 werraao@@iliioi.eeeee illinois.edu>,, Yangyi Chen <yangyic3 @ Illinois.edu>>
edu,, Chi Han <chihan3 @ illinois.edus: Sha Li < <manling notieetttttttt..ddu...... Heng Ji Manling Li <hengji eduu..is.edu>..
These collaborative coding agents are typically designed and evaluated in static environments where the workspace remains fixed throughout task execution (Jimenez et al., 2023; Yang et al., 2024a). However, real-world collabora- tive software engincering (CSE) fundamentally operates in dynamic environments, where effective teamwork depends
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada, PMLR 267, 2025. Copyright 2025 by the author(s).

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Package Upgrades
Caller Updates
Callee Updates
Dependency Updates
peees,
Hilae pessec la Kiie
Ficles, sintag
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Figure 2. Typical Causes of Out-of-Sync. Examples of of scenarios in our benchmark.
on team members maintaining synchronized awareness of workspace states-a a core challenge in the field (Yang et al., 2024b). While version control systems (Torvalds, 2005) can detect surface-level code conflicts, they cannot identify se- mantic inconsistencies that require manual resolution. This includes scenarios where agents must resolve dependency updates, modify existing functions align with newly im- ported modules, and so on (Fig. 2).
Based on SyncMind, we construct SyncBench ($3), testbed to assess agent out-of-sync recovery in CSE. Built upon 21 GitHub repositories, SyncBench simulates real-world agent oul-of-sync through commit history traversal and multi- level filtering to obtain 24,332 instances with executable testing environments. The construction pipeline is fully open-source and scalable, enabling seamless integration with additional repositories and supporting future develop- ment of CSE agents. Through systematic evaluation ($3.4), our experiments on SyncBench reveal fundamental patterns in existing L.LM-based software agents ($4)::
In this work, we introduce SyncMind ($2), framework that systematically defines the agent out-of-sync problem in CSE (Fig.3) 3), where multiple collaborators frequently modify and update shared codebases. This occurs when a collaborator's s belief state (B²) deviates from the actual world state (Sk) at time Tr. resulting in collaboration failures due to outdated information. Consider human-AI collaboration scenario in Fig. I: while an Agent implements changes based on its understanding at time T,, the Human modifies the codebase at T, (T; < <T₃- < Tş). The Agent's 's subsequent update at Tk becomes incompatible with the current state S₃ due to its outdated belief state Bg. This raises the key challenge: How can collaborators effectively recognize their belief being out- of-sync (Bx F Sy), diagnose the root causes, and recover their belief Bg to match the world state Sy?
Out-of-syne recovery capabilities: Evaluated by five metrics ($3.4) focusing on dissimilar aspects of agents' abilities, we observe substantial ability gaps among LLM agents that persist in their performance despite varying types of recovery actions and task complexity ($4.2). This highlights the significance of strong multifaceted capabil- ities for effective out-of-sync recoveries ($4.3).
Collaboration willingness and abilities: Collaborator as- sistance generally improves agents' recovery performance (0.33% S Acollaborator I 5.52%), while its effectiveness varies significantly with agents' collaboration willingness ($4.4) and communication abilities (54.5-4.6).
Resource awareness and adaptive utilization: Our ex- periments reveal critical limitations in agents' resource awareness and adaptive utilization efficiently utilize available resources when provided with various temporal and financial resource constraints ($4.7).
SyncMind facilitates multi-dimensional evaluation of collab- orative coding agents:
Out-of-sync recovery effectiveness ($4.2): We evaluate how agents detect and resolve state misalignments via exploring the environment and consulting fellow devel- opers, enabling them to understand system changes and resynchronize after failures.
2. SyncMind: Agent Out-of-Syne Recovery
Collaborative tendency and effectiveness ($4.5): We measure agents' tendency to engage in productive inter- actions with collaborators, a critical problem in CSE. By analyzing the assistance seeking rate and the performance difference in independent and collaborative working set- tings, we measure agents' recovery effectiveness in CSE.
Tackling agent out-of-sync 1), we introduce our Sync- Mind framework (Fig. 1) to systematically measure agent out-of-sync recovery in key dimensions: recovery effec- tiveness via two types of recovery ($2.2) and resource effi- ciency through resource-aware out-of-syRc recovery ($2.3).
Environmental awareness and resource allocation ($4.7)): We examine how agents balance independent problem-solving (i.e., exploring environment) with collab- orative assistance. While excessive self-reliance in debug- ging can strain computational resources, over-dependence on peer support can burden collaborators through repeti- tive cycles of assistance requests, revisions, and testing. We evaluate resource allocation strategies in out-of-sync recovery by analyzing recovery efficiency, considering computing time and expense budget.
2.1. Definition of Agent Out-of-Sync
In collaborative environments, state of 'out-of-sync" arises when a collaborator's 's belief state deviates from the project's state due to missed updates from other team members (Fig. 1). We propose the formal definition of 'out-of-xync' state, which applies to both human and Al agents.
Let S, be the true world state at time T,. and B, be an agent's belief state. Starting from T, when the agent begins
2

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Initial State
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Figure 3. SyncMinn for Measuring Agent Out-of-Sync Recovery. Depending on agent's 's initiative, an agent updates its world helicf
(B2 + B.) by interacting with Env, proposing solutions, and seeking collaborator assistance.
task, the agent becomes out-of-sync at Th (T; < Th) if any of the following conditions are satisfied:
hypothetical cost that quantify financial resources consumed through the course of recovery (e.g., computing resources for debugging and testing, Human's 's time and effort to an- swer Agent's i questions). This sesorree-awar out-of-sync recovery framework measures how agents utilize and adapt their strategies under different resource constraints, enabling comparisons of efficiency between successful and failed re- covery attempts across agentic systems.
(1) Knowledge gap: Update U at time T (T, < T Ti
where the agent lacks knowledge of U.
(2) State mismatch: B& # Sk-
(3) Task failure: Task completion based on Bg fails to achieve intended outcomes in Sk.
Recovery from an out-of-sync state therefore requires:
(1) Identify the root causes (U) of the state mismatch
Aisource-Awart Out-of-Sync Recovery
(Bx+ * Sz).
Resources
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2.2. Agent Out-of-Sync Recovery
Figure 4. Resource-Aware Out-of-Syne Recovery. We introduce resource- aware recovery by mapping resource consumption to each out-of-syne recovery task.
In SyncMind (Fig. 3), an agent updates its out-of-syne belief state to attain B, = S,, through two types of recovery:
Independent Recovery. Operating autonomously, inde- pendent agents update their world beliefs through inter- acting with Environment (Env) and proposing solutions, besides their reflection on prior experience and feedback. Collaborative Recovery. Collaborative agents can also take advantage of collaborator assistance to update their belief states by interacting with other collaborative agents.
3. SyncBench: Agent Out-of-Syne Benchmark
3.1. Benchmark Construction
Aligning with real-world out-of-syne scenarios, our bench- mark construction method is applicable to Python-based GitHub repositories with existing unit tests. SyncBench leverages 21 popular GitHub repositories and can be expanded to include additional repositories following our benchmark construction methodology ($B.3). In accordance with the definition of agent oul-of-syne 162. our benchmark construction implements a systematic pipeline that takes all three conditions into consideration:
2.3. Resource-Aware Recovery
To reflect real-world resource constraints in collaborative environments, we integrate a resource awareness module into SyncMind (Fig. 4). This module tracks and constrains two dimensions of resources: (1) recovery time measured as the number of turns taken for an agent to recover, and (2)
Env Configuration. We employ Docker (Founadi et al., 2013) to configure isolated, reproducible, and executable
3

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
GitHub Repository
tests/
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Figure 5. Agent Out-of-Syne Benchmark Construction. A systematic benehmark construction approach ($3.1).
testing environments tailored for our out-of-sync recovery tasks. Each source repository is packaged into a dedicated Docker image with complete codebase, dependencies, and validation infrastructure for unit test execution. Each exe- cution verification ($3.4) automatically creates isolated container instance with auto-removal upon completion, en- suring consistent and clean testing environments for reliable recovery evaluation.
15 instances while maintaining the original patch distribu- tion over all sampled data, thereby applying the same task complexity distribution to all downsampled instances.
3.2. Benchmark Datasets
Constructing SyncBench with two complementary datasets- Caller and Callee (Fig. 5), our initial extraction yields 24,332 instances (Tab. B2). Pruning the raw dataset to 8,461 instances via multi-level filtering, the evaluation sub- set is further reduced via weighted downsampling. As such, we finalize our evaluation samples as 300 instances with evenly distributed Caller and Callee samples (150 each).
Out-of-Syne Simulation. We first extract Python functions and class methods (hereafter collectively referred as func- tions) from source repositories. For each extracted func- tion, employ its up-to-date state as ground truth (S2), while obtaining the out-of-sync belief state (B2) by tracing its Git history reversely until identifying a commit (B2) where execution fails (B₂ * S2). In this way, Caller and Callee are constructed through simulating unit test out-of- sync and tested dependency out-of-sync, respectively: (1) Caller: We roll back the testing function until it becomes out-of-sync; (2) Callee: We roll back imported dependency for tested module out-of-sync, thereby presenting higher task complexity- agents need to understand dependency re- lationships and localize the problematic imported modules,
3.3. LLM-Simulated Collaborators
We leverage LLMs to simulate both agents (who enter out- of-sync states B2) and know-everything collaborators (S2).
Agent Out-of-Sync. We employ LLMs to power Al agents in out-of-syne states, which allows belief states to become tractable and controllable throughout the recovery process. Meanwhile, this also supports the precise measurement of an agent's 's resource consumption and the systematic evaluation of an agent's 's recovery patterns.
Multi-level Quality Filtering. For each out out-of of--of-yync in- stance, we execute unit tests before and after out-of-syne hap- pens and use the parsed test outputs filter for high-quality instances. Our parsing -based execution testing ($3.4) re- quires the pass-to-fail state divergence (B2= + S2): (1) up- dated repository (S1) passes the test to demonstrate ground truth validity, and (2) repository with the out-of-syne func- tion (B2) fails the test to allow the out-of-syne scenario to take shape. To enhance data quality, we additionally apply a filter that retains only instances with their execution outputs comprising: (1) least one execution error or unit test fail- ure in B2, (2) more than one passing test in S1, (3) identical parsing result between S₁ and S,.
Simulating Know-Everything Collaborators. Validated by single-turn experiments (54.4), LLM-simulated know- everything collaborators are furnished with: (1) complete task context, including both By B, and So S, (where the agent seek assistance at T₁, 2 < t n), (2) ground-truth solution to reach B, = S.. (3) update history (U at Ti), and (4) task-specific response protocols ($D.5).
3.4. Evaluation Metries
We propose five complementary metrics tailored for com- prehensively evaluating agent out-of-syne recovery:
Weighted Downsampling, In constructing our evaluation subset with 300 representative instances! across 21 reposi- tories, we downsample each repository's 's data less than
Success Rate (SR). We evaluate recovery success (Eq. 1) through a two-stage validation process: (1) Execution Test: Execution success can be reached only if agent's updated repository passes the test without errors (i.e., command exit code of 0). (2) Parsing Validation: We compare the parsed test execution outputs of an agent's 's proposed solu- tion against that of the ground state (i.e., the original
Due to the costly expenditure of extensive model evaluations (on average $0.56/ 50.56//nstance, iistance, ranging from GPT-4o mini with S0.02 iiisttnnce to Claude-3.5 S.5-omnett with $1.73 73/instance,, we downsample subset of SyncBench with 300 instances ($3).

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
commit without issue). Recovery success requires all parsed output of test cases for the agent-proposed solution to actly match the ground-truth values.
4. Experiments
4.1. Setup
Recovery Protocol. For baselines, each agent is allowed up to 30 turns to achieve B,, = S., which is then extended to 50 turns to assess agents' temporal resource awareness and exploitation. Financial resources to each resource-aware recovery task. Provided with dif- ferent action options-interactoo interactiin with Env, proposing a solution, or proactively seeking collaborator assistance ($2.2) both independent and collaborative agents take each of their moves autonomously.
SR = EmEm 1S 1)
(I)
EmEM 1
where M represents the task space, S.m E {0, 1} suggests whether task m achieves recovery success, 1()) is the indicator function that returns when the condition is met and O otherwise.
mapped similarly
Localization Accuracy (LA). We evaluate an agent's abil- ity to localize out-of-sync function at two levels: (a) file (LA (ue): accurately identifying the Python file containing the out-of-syne function; and (b) function (LAfune) accu- rately pinpointing the specific out-of-sync function.
Env Space. We employ OpenHands (Wang et al., 2024a) to empower agents autonomously explore and inspect the codebase environment by executing various commands. This exploration enables them to develop comprehensive understanding of the codebase for out-of-sync recovery.
LA EM 1LL pm =1
EmEMl 1
Agents. Our experiments assess the out-of-sync recovery ca- pabilities of seven LLMs, including four open-source (Meta- Llama-3. 1-8B,, Meta-Llama-331-7000 Meta-Llama-3.3-17
where J E [file, fune) denotes the localization target type, LA E {0, 1} indicates whether task achieves localization success for target type f.
and DeepSeek- V2.5) and three close-source (Claude-3.5- Sonnet, GPT-4o mini, and GPT-4o) LLMs (Meta AI, 2024b;a;;; DeepSeek, 2024; Anthropic, 2024; OpenAl, 2024a;b) in two recovery settings ($2.2), respectively.
Conditional Success Rate (CSR). We evaluate agents' tech- nical recovery abilities by conditioning recovery on local- ization success, which leads to CSR file and CfRRuunc
CSR, LA EmEm 1(SRm 1A LA 1) 1)
Indecceneett Colioberativ
Emem 1(LAf.m = 1)
21.95
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Assistance Seeking Rate (ASR). We quantify a collabora- tive agent's willingness to collaborate as the proportion of
recovery time (measured in turns of interactions) it adopts for proactive assistance-seeking
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where T represents the recovery time space, Ta (m E M) sug- gests the total time in task m, AS, E f0, 1} indicates whether the agent seeks collaborator assistance in turn L.
LA m11
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Recovery Efficiency. We compute the ratio of turns taken to the maximum time limit as the proxy for time efficiency, thus excluding external influencing factors, like connection stability and memory capacity. Expense efficiency is simi- larly calculated as the average financial expenditure rates.
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where g [lime, expense) denotes the efficiency type, Prime (m)) Eierm (ar") counts turns taken for task m,
28.10
Despensee (m) = LETT c(a?") sums costs of actions for task m, Usime (m) = Tmaz is the maximum time limit, Vexpense (m)) = Cmax is the maximum budget, at" is the action taken at step in task m. and c(a) is the cost function for action a.
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Figure 6. Influence of Collaborator Assistance. We quantify
collaborator influence on agent out-of-sync recovery performance as Avvilaboonn to unveil its +positive -negative impact on certain aspects of agents' recovery performance.
5

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Table 1. Out-of-Sync Recovery Evaluation on Caller and Callee. The influence of increased task complexity introduced by dependency
tracing on agents' out-of-sync recovery performance: Aompleciiy
(Callee-Caie Calfer)
Caller (%)
Callee (%)
complesity (%)
Agent
Recovery
LAgae
LAfune
SR
LAfat
LAfune
SR
LAysi
LAfune
SR
Independent
13.33
8.00
1.33
4.00
-9.33
-6.67
-0.66
Llama-3.1-8B
Collaborative
32.00
26,00
2.00
22.00
16.67
0.67
-10.00
-9.33
-1.33
A collaborator
+18.67
+18.00
+0.67
+18.00
+15.34
+0.00
-0.67
-2.66
-0.67
Independent
8.67
5.33
4.00
8.00
4.67
1.33
-0.67
-0.66
-2.67
Llama-3.1-70B
Collaborative
12.00
6.00
3.33
12.67
5.33
3.33
+0.67
-0.67
+0.00
Avollaborator
+3.33
+1.33
-0.67
+4.67
+0.66
+2.00
+1.34
-0.67
+2.67
Independent
13.29
9.30
5.32
7.97
6.64
2.66
-5.32
-2.66
-2.66
GPT-4o mini
Collaborative
15.28
11.96
7.97
9.30
4,65
2.66
-5.98
-7.31
-5.31
Avoiloboratoo
+1.99
+2.66
+2.65
+1.33
-1.99
+0.00
-0.66
-4.65
-2.65
Independent
58.00
47.33
8.67
37.33
22.67
6.00
-20.67
22.66
-2.67
DeepSeck
Collaborative
52.00
47.33
8.67
42.00
27.33
-10.00
-20.00
-2.00
collobbratto
-6.00
+0.00
+0.00
+4.67
+4.66
+0.67
+10.67
+4.66
+0.67
Independent
14.67
11.33
6.67
14.00
7.33
1.33
-0.67
-4.00
-5.34
GPT-4o
Collaborative
39.33
35.33
10.00
38.67
34.00
6.00
-0.66
-1.33
-4.00
coolloborator
+24.66
+24.00
+3.33
+24.67
+26.67
+4.67
+0.01
+2.67
+1.34
Independent
80.67
60.00
18.67
47.33
34.67
14.00
-33.34
-2.33
-4.67
Llama-3.3-70B
Collaborative
77.33
64.67
22.00
56.00
42.67
16.00
-21.33
222.00
-6.00
collaborator
-3.34
+4.67
+3.33
+8.67
+8.00
+2.00
+12.01
+3.33
-1.33
Independent
50.83
47.51
25.41
77.35
65.19
30.94
+26.52
+17.68
+5.53
Claude-3.5-ooneet
Collaborative
43.09
38.67
28.73
79.56
65.19
38.67
+36.47
+26.52
+9.94
Avolleborator
-7.74
-8.84
+3.32
+2.21
+7.73
49.95
+8.84
+4.41
4.2. Significant Ability Gaps Among Agents Powered by Different LLMs
4.3. In Achieving Recovery Success: Technical, Reasoning, and Collaborative Competences
Our experiments on SyncBench (Tab. 1-C1) reveal substan- tial capability gaps among seven LLM agents.
Conditioned on localization success, CSR (Eq. 3) is sig- nificantly influenced by how much time left for technical recovery after accurate localizations, which are largely de- termined by agents' abilities to efficiently identify root causes of B, + Sy- Comparing Tab. 1-C1 with Tab. C2- C3, low-performing agents can also showcase strong tech- nical problem-solving capacities (e.g., CSR: Llama-3. I- 70B > GPT-40), despite their notably underperformed localization and recovery abilities (e.g.. Llama-3, 1-70B: LA S 12.33%, LAfume S 5.67%, SR S 3.33%) and remarkably low willingness to collaborate (e.g., Llama-3.1 70B: ASR 1.37%). This observation further substanti- ates that successful out-of-sync recoveries hinge on not only agents' technical problem-solving proficiency, but their effi- cient cause analysis and effective collaboration capabilities.
Baselines for Out-of-Syne Recovery. LLM agents' inde- pendent out-of-sync recoveries demonstrate significant vari- ations in their baseline capabilities, ranging from Claude- 3.5-Sonnet (SR- 28. 18%) to Ilama-3./ agents (SR < 2.67%). Their localization capabilities also vary remark- ably, regardless of pinpointing the exact out-of-sync func- tion (LAfunc E [4.67, 56.35]% or less precisely localiz- ing responsible Python files (LAfitc E [8.33, 64.09)%). Likewise, our evaluation on agents' technical capabilities (Tab. C2) also exhibits substantial gaps among LLMs.
Persistent Gaps Despite Varying Recovery Conditions. Tab. CI and Tab. C2 show similar performance disparities for collaborative agents mirroring their independent recover- ies, despite the generally positive influence of collaborative assistance. These performance gaps remain significant for tasks of different complexity (Tab. & C3). Agents' per- sistent performance variances across diverse task scenarios highlight their underlying ability gaps in identifying and resolving out-of-sync to maintain effective collaborations.
4.4. Collaborative Assistance Improves Performance- But Agents Seldom Seek Help
Positive Collaborator Influence. As shown in Fig. 6, col- laborator assistance generally improves recovery perfor- mance (SR + ( 5.52]%), with the mag- nitude varying dependent on agents' technical capabilities
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
and willingness to collaborate. Agents with stronger inde- pendent recovery capabiliites and collaboration willingness (Claude-3.5-Soneee SR = 28. 18% and ASR = 4.86%),
Results reveal that the proportion of assistance seeking in agents' first half of recovery time is substantially larger in success cases (85. 71% 100.00%) than in recovery fail- ures (55.76% - 97..% As top- cop-performing agents exhibit distinct communication strategies with front-load queries, random or back-loaded assistance-seeking demonstrates less effective improvements on agents' performance. Compared with solution proposal timing that shows trivial differences between success and failure cases (averagely 2.79 turns delayed in successful recoveries), collaborative agents bene- fit markedly more from advancing their assistance seeking (averagely 10.50 turns ahead in successful recoveries).
together with conditioned technical proficiency (Tab. C2),
obtain higher performance gains coolibbonttor = +5.52%)
than other agents (SR= < 4.00% and ASR S 2.98%).
Solving task with Oracle Information. To establish the theoretical upper bound of collaborator influence and identify agents' collaboration capability gaps, we additionally conduct a single-turn exper- iment by providing agents with oracle information that is used to simulate the collaborator (i.e. the know-everything agent). With GPT-4o mini as the agent tackling out-of-sync and GPT-4o as the know-everything collaborator ($3.3), we configure each single-turn task with collaborator's is exhaus- tive task-specific natural language instructions how to accomplish recovery success. Furnished with full recov- ery instructions (ASR- = 100%), the high upper bound (SR- = 86.33%) lends further evidence to LLM agents' significantly untapped potential in effective collaboration.
Performance Upper Bound:
Proportion of High- Quall-y Quastbonn (K0)
High-Quailty Questions On Code Localizatton High-Quaiity Questions On Recovery Solution
Lie (S)
Uite (%)
16,00
Roluation Metrics (N)
(%)
20
8.77
7.25
5.71
6.79
21:35
LLM Agents' Low Willingness to Collaborate. Despite strong technical capacities (Tab. C2), LLM agents show limited collaboration willingness (Fig. 7). Nevertheless, Claude-3.5 with the highest performance (SR- = 33.70%) and collaboration willingness (ASR- = 4.86%) derives the most benefit from collaboration (A (Avolaaorntor = +5.52%) is followed by GPT-40 who obtains notable improvements in both LA (Acollabontor = +25.34% and SR (Acollabrattor +4.00%) through proactive assistance seeking. The lowest ASR 1.21% % presented by DeepSeek (SR: coollbboatto = -0.34% contrastingly substantiates the significance of proactive collaboration initiative.
5,00
6.74
2.38
2.78
5.94
9.09
DeepSeak
Liama 3.1-708
GPT-4o mini
Liama
Liama 3.3-708
GPT-40 Claude-3.5
3.1-8B
Sonnet
Figure 8. Question Quality, Agents from left to right on the X- axis according to their ASR from low to high.
4.6. More Challenging Tasks Decrease Performance While Better Manifest Collaboration Benefits
We observe a large negative influence of increased task complexity on agents' recoveries. Callee's additional de- pendency tracing allows it to serve more challenging out-of- sync tasks ($3.1). Comparing agents' performance between Caller and Callee (Tab. I & C3), Claude-3.5 3.tedee-.5-Soneeeee --Sonnee''s 's per- formance gains (+ complexity demonstrate its superior tech- nical capabilities in resolving complicated out-of-syne tasks. Nevertheless, Callee, presenting higher task complexity, in general undermines agents' performance (-Acompieity).
4.5. Quality and Strategy of Communication Are Crucial for Recovery Success
The quality and strategy of communication prove crucial for recovery success, with several key patterns emerging:
Agents with More High-Quality Questions Achieve Bet- ter Performance. Depending on whether the question asked by the agent can lead to recovery success ($C.4), we rate the quality of each query as low (resulting in recovery failure) or high (resulting in recovery success), which can be further classified into two general categories: localization queries closely related to localizing out-of-syne causes and solution queries seeking guidance on out-of-sync resolution. Despite no significant correlation between query volume and recov- ery success, agents with a larger proportion of high-quality questions achieve higher performance (Fig. 8).
Leveraging dissimilar complexity levels of 21 source repos- itories (Fig. C8), our repository-wise evaluation reflects consonant patterns between task complexity and recovery success. While the repository 11-whi sper proposes the least recovery difficulty (SR: Independent 33.33%, Collab- orative 22. 22.22%), the lowest performance delivered on the repository 13 13-sphinx (S (SR: Independent 0.88%, Collab- orative 4.70%) serves more challenging tasks.
Although repository complexity manifests negative correla- tions with recovery success, the effectiveness of collabora- for assistance increases on more challenging tasks, com- paring to its trivial or negative influence on agents' recov- cries in simpler out-of-sync scenarios (e.g.. 11-whi sper with Acollaborator = 11.11%% in contrast to 13 13-sphing i nx
Strategic Early Exploration Facilitates Recovery Success. We compute each agent's 's communication timing distribu- tion respectively for its success and failure cases (Fig. CI).
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Recovery Setting
Collaborative
Independent
Assisiance Seoking Fate (ASR)
Solution Propocal Rate
Env interaction Rate
100
68.02
69.54
77.53
84.05
Time Allocation (%)
50
20.90
29.09
1.37
2.98
19.49
2.92
15.03
4.86
6.08
-25
0.00
0.00
29.96
0.00
1.30
0.00
22.53
0.00
5.43
0.00
15.08
0.00
6.52
-50
-100
-75
70.04
98.70
77.47
91.59
04.92
93.48
Llama-3.1-88
Llama-3.1-708
GPT-4o mini
GPT-4o
DeepSeek
Liama-3.3-708
Claude-3.5 Sonnet
1:33
3:38
32
67
39 70
(%)
0.33
2.67
3.99
4.00
7:33
16. 33
28. 1B
Figure 7. Time Allocation. Agents' performance ranked from low to high according their independent SR scores, based which they are positioned on the X-axis from left to right. The Y-axis depicts each agent's time allocation.
with coliabonntor = +3.82% This holds in Callee where agents notably gain more benefits from collaborator assis- tance (Tab. 1), despite higher task complexity.
for doubled cost, Tab. C7). These findings highlight the fun- damental deficiencies of existing LLM agents in effectively recognizing resource constraints and adaptively leveraging available resources (Yang et al., 2024b).
4.7. Agents Significant Lack of Resource Awareness
5. Related Work
We systematically vary resource constraints (Tables C5-C8) to investigate L...-based agents' resource awareness in two key dimensions ($2.3): (1) Time Resources, through comparing the standard 30-turn recovery with extended 50- turn performance; (2) Financial Resources, through varying initial budgets from $1000 (insufficient for 30-turn costs) to $3000 (adequate for any 30-turn action taking patterns), and halving or doubling the cost of collaborator assistance. Our experiments reveal critical limitations in agents' resource awareness and adaptive resource utilization capabilities.
5.1. Theory of Mind in Collaborative Systems
The concept of ToM-the ability to model and reason about others' mental states-has emerged as the foundation of collaborative systems. LLMs' ToM capabilities have been evaluated and applied to various domains through bench- mark construction (Chen et al., 2024b; Xu et al., 2024; Wu et al., 2023; Kim et al., 2023) and experimental investigation (Amirizaniani et al., 2023; Chen et al., 2024a; Strachan et al., 2024; Kosinski, 2023; Wilf al., 2024; Verma et al., 2024). We apply ToM to software engineering agents for their main- taining of a shared understanding of codebase states, which is particularly of critical essence in asynchronous collabora- tion environments where out-of-syne situations frequently arise due temporal gaps between contributions.
More Recovery Time Can Not Guarantee Performance Gains. Extended time produces divergent effects (Fig. C3 & Tab. C5): diminishing returns Llama-3.1-8B (SR: Independent -0.33%, Collaborative - -1.00)) while no- table improvements on Llama-3.1-70B (SR: Independent +3.67%, Collaborative +4.67%). This observation sug- gests that extending the recovery time limit alone insuffi- cient for improved performance, while I.IM agents' com- petences in effective time utilization and technical expertise (Tab. C2) factor underlyingly into recovery success.
5.2. Collaborative Software Engineering Systems
The evolution of software engineering tools and practices relies heavily on the premise of synchronized collaboration. Modern version control systems (Torvalds, 2005; Owhadi- Kareshk et al., 2019; Machowski & Marwala, 2021), al- though implement sophisticated mechanisms for detecting and resolving conflicts arising from divergent codebase states, primarily address syntactic conflicts rather than se- mantic understanding divergence. Recent work showcases that I.IM reasoning can be effectively advanced by diverse means, such as human feedback (Wang al., 2024b; Bal- loli et al., 2024), Env interaction (Wang et al., 2024a; 2025; Zhang et al., 2024), multi-agent cooperation (Huang et al., 2024), etc. Pressing closer to real-world repository-level
Agents' Low Sensitivity to Financial Resources. Tripling the initial budget yields trivial changes in action plan- ning (ASR improvements: [+0.22, + +1.06]% %, Fig. C6) and recovery performance (SR. variations: [-0.66, +.,77]% Tab. C6). Similarly, halving or doubling the action cost of assistance seeking contributes to negligible differences in
L.M agents' willingness to collaborate (A.RR variations: [-0.11, +0. 0..0..3222 % for halved cost, [-0.46, +0.04]% for dou- bled cost, Fig. C7) and recovery performance (SR varia-
tions: 2200 - 1.00]% for halved cost, [11000, +1000%
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
programming, advances in LLMs inspire their agentic en- gagements in software engineering (Liu et al., 2024), in addi- tion to benchmarks built upon Gitllub repositories (Jimenez et al., 2024; Jain et al., 2024; Chen et al., 2025). However, real-world dynamic environments require adaptations for collaborative systems based on the presumption of relatively static environments. Agents' lack of automatic synchroniza- tion and resource awareness therefore arise latent obsta- cles that significantly impede collaborative intelligence and resource efficiency that we aim to provide insights into.
A Acknowledgement
This research is based upon work supported DARPA ITM Program No. FA8650-23-C-7316 and the AI Research Insti- tutes program by National Science Foundation and the Insti- tute of Education Sciences, U.S. Department of Education through Award # 2229873 Al Institute for Transforming Education for Children with Speech and Language Process- ing Challenges. The views and conclusions contained herein are those of the authors and should not be interpreted as nec- essarily representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for govern- mental purposes notwithstanding any copyright annotation therein.
6. Conclusion and Discussion
In this paper, we investigate the out-of-sync challenge in collaborative software engineering by introducing our out- of-sync recovery framework, SyncMind $2, and evaluation benchmark, SyncBench $3. Experiments reveal that suc- cessful out-of-sync recoveries require not only technical proficiency ($4.2-4.3), but also effective collaboration ($4.4- 4.5) and adaptive resource management ($4.7) abilities. Based on our ovaluation of multiple aspects of their recov- ery performance ($3.4), results unveil existing L.M agents' limited collaboration willingness and resource awareness, providing insights for future development of collaborative systems with stronger collaboration initiative and cooper- ation competences, along with more adaptive resource uti- lization capacities. Detailed discussions of our findings and limitations are presented in Appendix A.
Impact Statement
This work aims to provide meaningful insights into advanc- ing collaborative Al systems and their application in soft- ware engineering, with potential societal implications in several areas. The framework and findings serve to im- prove the reliability and efficiency of collaborative software engineering, potentially reducing costly errors and develop- ment delays. However, there are important considerations valuable take into account. First, while enhanced collab- oration capabilities of AI agents could improve software quality and developer productivity, they may also impact human developers' jobs and require careful integration into existing workflows. Second, our resource-awarg framework highlights the need to consider computational and environ- mental costs in deploying collaborative Al systems at scale. Additionally, as Al agents become more capable of detecting and recovering from synchronization issues, it of signifi- cance to ensure that human developers maintain meaningful oversight and understanding of system changes. We believe these considerations should be actively discussed as the field moves toward more sophisticated collaborative AI systems in software engineering.
References
Amirizaniani. M., Martin, E., Sivachenko, M., Mashhadi, A., and Shah, C. Do llms exhibit human-like reason- ing? evaluating theory of mind Iims for open-ended responses. arXiv preprint, abs/2302.02083, 2023. 8
Anthropic. Claude, 2023. URL https:/ //www.
anthropic. ctm/ claude.
Anthropic. Claude-3.5 Sonnet, 2024. URL https://
www. www.roptthhi...o comm//lauee////meeee......
Balloli, V., Beery, S., and Bondi-Kelly, E. Are they the same picture? adapting concept bottleneck models for human-ai collaboration in image retrieval. 2024.

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Chen, J., Zhao, K., Liu, J., Peng. C., Liu, J., Zhu, H., Gao, P. Yang, P., and Deng. S. Coreqa: Uncovering potentials of language models in code repository question answer- ing, 2025. URL https: / 03447.9
Singapore, December 2023. Association for Computa- tional Linguisties. doi: 10.1865/w//2233emmp-mmaa 890. URL https: /aa//aaaanttt www.aannnnoooooooooooooooooooooe wtgp:/233...332333
8
Kosinski, M. Theory of mind may have spontaneously emerged in large language models. arXiv preprint, abs/2302.02083, 2023.
Chen, Z.. Wang, T., Wang, Y., Kosinski, M., Zhang. X., Fu, Y., and Li, S. Through the theory of mind's eye: Reading minds with multimodal video large language models. ArXiv, abs/2406.13763, 2024a. 8
Liu, J., Wang. K., Chen, Y., Peng, X., Chen, Z., Zhang, L., and Lou, Y. Large language model-based agents for soft- ware engineering: A survey. ArXiv, abs/2409.02977, 2024. URL https: / http//appp...aaiiieeeeeeeeeme Ptgp/oorpaaD::21222377 sewanti
Chen, Z., Wu, J., Zhou, J., Wen, B., Bi, G., Jiang, G., Cao. Y., Hu, M., Lai, Y.. Xiong. Z., and Huang, M. ToMBench: Benchmarking theory of mind in large lan- guage models. In Ku, L.-W., Martins, A., and Srikumar, V. (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp- 15959 15959-15983, Bangkok, Thailand, Au- gust 2024b. Association for Computational Linguistics. URL https://
Machowski, L. and Marwala, T. Nano version control and "robots of robots" data driven, regenerative production code. 2021 International Conference Computational Science and Computational Intelli- gence (CSCI), pp. 1938-1943, 2021. URL https:
doi: 10. 10.18655kw112022.ac--ooggg. 8477.
238582878. 8
Cognition AI. Devin, 2024. URL URU
Meta AL Meta-LLaMA-311-00B, 2024a.
URL
https:/hhuginggface iett--llaae
DeepSeek. DeepSeek V2.5, 2024. URL https:// api-docs deepseek com/ httm//nww//eeeeee news//nws0990.5
Llama-3.1-70B.
Meta AL Meta-LLaMA-331-88. 2024b.
URL
Founadi, K., Hykes, S., and Pahl, S. Docker, 2013. URL https:/ //www docker. com./
https: httgss///hgggnggfcceec co co/meta-1lan
Huang, D., Zhang, J. M., Luck, M., Bu, Q., Qing, Y., and Cui, Agentcoder: Multi-agent--asee code gen- eration with iterative testing and optimisation. arXiv preprint arXiv:23/2.13010 2024. doi: 10.48550/arXiv.
Meta AL Meta-LLaaMA-333700. 2024c.
URL
https: https://hugginggaace co/meta 1lamaa
2312.13010. 8
OpenAl. ChatGPT, 2022. URL https://openai.
Jain, N., Shetty, M., Zhang, T., Han, K., Sen, K., and Stoica, R2e: Turning any github repository into programming agent environment. In ICML, 2024.
comm/hhatgpt/overiiee///
OpenAL. GPT-4o, 2024a. URL https:h//ptttaaaeeee
40.
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and Narasimhan, K. Swe-bench: Can language mod- els resolve real-world github issues? arXiv preprint vrXiv:2310.06771 2023.
OpenAL. GPT44o mini, 2024b. salaatorr.oppeaaal com/ /ocs /wo//ooolll/e/ 3ot-4o-
URL https:
Jimenez, C. E., Yang, J., Wettig. A., Yao, S., Pei, K., Press, O., and Narasimhan, K. Swe-bench: Can lan- guage models resolve real-world github issues? arXiv preprint arXiv:2310.06777,, 2024. doi: 10.48550/arXiv. 2310.06770. 9
Owhadi-Kareshk, M., Nadi, S., and Rubin, J. Predicting merge conflicts in collaborative software development. 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pp.
11, 2019. URL https: / /api. wwtaaapp...aammtm.............ee semanticscholar. org Corpus 19: 8
Kim, H., Sclar, M., Zhou, X., Bras, R., Kim, G., Choi, Y., and Sap, M. FANToM: A benchmark for stress- testing machine theory of mind in interactions. In Bouamor, H., Pino, J., and Bali, K. (eds.), Proceed- ings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 14397-14413,
Strachan, J. W. A., Albergo, D., Borghini, G., Pansardi, O., Scaliti, E., Gupta, S., Saxena, K., Rufo, A., panzeri, S., Manzi, G., Graziano, M. S. A., and Becchio, C. Testing theory of mind in large language models and humans. Nature Human Behaviour, 8: 8:1255-1295 2024.
10

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Torvalds, L. Git, 2005. URL http://:://iit 2,8
717. URL httpss//cclaaatthee woweehhhooooo..ooooo ogg.olg/200.... f nd nng.e-nee.111..777... 8
Verma, M., Bhambri, S., and Kambhampati, S. The- ory of mind abilities of large language models in human-robot interaction: An illusion? In Compan- ion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, HRI '24, pp. 36-45, New York, NY, USA, 2024. Association for Comput- ing Machinery. ISBN 9798400703332. doi: 10.1145/ 3610978.3640767. URL https://doi.... org/10. 1145/3610978.3640777..
Xu, H., Zhao, R., Zhu, L., Du, J., and He, OpenToM:
A comprehensive benchmark for evaluating theory-of- mind reasoning capabilities of large language models. In Ku, I..W., Martins, A., and Srikumar, V. (eds.), Proceed- ings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), PP- 8593-8623, Bangkok, Thailand, August 2024. Associa- tion for Computational Linguistics. doi: 10.18653/v1/ 2024.acl-long 466. URL https: /t/////aaaantoooooo/. acg/2024.a111oonn. 466/. 8
Wang, X., Li, B., Song, Y., Xu, F. F., Tang, X., Zhuge, M., Pan, J., Song. Y., Li, B., Singh, J., Tran, H. H., Li, F.,
Yang, J., Jimenez, C. E., Zhang, A. L., Lieret, K., Yang, J., Wu, X., Press, O., Muennighoff, N., Synnaeve, G., Narasimhan, K. R., et al. Swe-bench multimodal: Do ai systems generalize to visual software domains? arXiv preprint arXiv:2410.03859, 2024a.
Ma, R., Zheng, M., Qian, B., Shao, Y., Muennighoff, N., Zhang, Y., Hui, B., Lin, J., Brennan, R., Peng, H., Ji, H., and Neubig, G. OpenHands: An open platform for ai software developers generalist agents. 2024a. doi: 1, 5,
10. 00.44550/rrXi..4477.10
Yang, Z., Liu, A., Liu, Z., Liu, K., Xiong, F., Wang, Y., Yang, Z., Hu, Q.. Chen, X., Zhang, Z., Luo, F,, Guo, Z., Li, P., and Liu, Y. Towards unified alignment be- tween agents, humans, and environment. arXiv preprint. abs/2402.07744, 2024b. 2,8
Wang, X., Wang, Z., Liu, J., Chen, Y., Yuan, L., Peng, H., and Ji, H. Mint: Evaluating Iims in multi-turn interaction with tools and language feedback. 2024b.
Wang, X., Chen, Y., Yuan, L., Zhang, Y., Li, Y., Peng, H., and Ji, H. Executable code actions elicit better Ilm agents. In Proceedings of the lsst International Conference on Machine Learning, 24. JMLR.org, 2025. 8
Zhang, K., Li, Li, G., Shi, X., and Jin, Z. CodeAgent: Enhancing code generation with tool-integrated agent systems for real-world repo-level coding challenges. In Proceedings of the 62nd Annual Meeting of the Associ- ation for Computational Linguistics (Volume I: Long Papers), pp. 13643-13658, Bangkok, Thailand, Au- gust 2024. Association for Computational Linguistics. doi: 10. 10.18633w1/22222aclloooog 737. URL ht https:// wtwp:/howlooy.orr/22/2.ooo aclanthology. wrt.//22..000..... acl.aacllloo long. 737.
Wang, Z., Mao, S., WU, W., Ge, T., Wei, F., and Ji, H. Unleashing cognitive synergy in large language mod- els: task-solving agent through multi-persona self- collaboration. In Proc. 2024 Annual Conference of the North American Chapter of the Association for Computa- tional Linguistics (NAAC120024), 2024c.
Wilf, A., Lee, S., Liang, P. P., and Morency, L.-P. Think twice: prrspective-takingg improves large language mod- els' theory-of-
capabilities. Ku, L.- Martins, A., and Srikumar, V. (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8292-8308. Bangkok, Thailand, August 2024. Association for Com- putational Linguistics. doi: 10. 10.18653w1//2024.cdlloooo 451. URL https:/ http///annoooooooorg///2/22 hel-longe 451/.8 8
Wu, Y., He, Y., Jia, Y., Mihalcea, R.. Chen, Y., and Deng, N. Hi-ToM: A benchmark for evaluating higher-order theory of mind reasoning in large lan- guage models. In Bouamor, H., Pino, J., and Bali, K. (eds.). Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 10691-10706, Singa- pore, December 2023. Association for Computational Linguistics. doi: 10.18663/v 1/.02/...nnnl..... findings--mnnll

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
A. Discussions and Limitations
The evolution of collaborative software engineering introduces complex challenges in maintaining synchronization among collaborators, whether humans or Al agents. Our investigation into the agent out-of-sync challenge reveals fundamental insights into how collaborators detect, respond to, and recover from belief state divergence (Bx * Sy at time Ti.). This section elaborates our key findings, discusses their broader implications, and acknowledges important limitations that suggest potential directions for future research.
Key Findings and Implications. By introducing our evaluation framework (SyncMind $2) and benchmark (SyncBench $3) built upon real-world GitHub repositories, our experiments ($4) illuminate critical aspects of agent out-of-syne:
(1) Technical Capabilities and Collaborative Effectiveness. Through either independent or collaborative recovery ($2.2), the stark performance variations among LLM agents (Tables 1-C1 & C2-C3) demonstrate that technical proficiency alone is insufficient for successful out-of-sync recovery 3-4.2-4. &C.1). Similar recovery trajectories of tigh-performanc agents (e.g., (laude-3.5-Sommee with SR: Independent 28.18%, Collaborative 33.70%), especially with respect to early-stage exploration ($C.2) and proactive collaborative initiative ($4.4-4.5), suggest that both independent problem-solving and collaborative communication capabilities are crucial for maintaining synchronization in real-world dynamic collaborative environments.
(2) Collaboration Patterns and Communication Quality. Experiment results reveal the positive correlation between LLM agents' collaboration willingness and recovery success. Benefiting from proactive assistance seeking, the influence of collaborator assistance (Acollaborator E [0.33, 5.52]%) remains beneficial among different LLM agents. However, its effectiveness varies significantly as affected by communication timing (85.71% 100.00% early-stage assistance among successful recoveries), question quality (rather than volume), and proactive collaboration initiative (ASR S 4486%) (84.4-4.5).
(3) Task Complexity and Recovery Strategies. Agents' performance gaps between Caller and Callee tasks (Tab. 1) highlight how different types of out-of-sync scenarios require distinct recovery strategies ($4.5). While Callee tasks requiring dependency tracing show wider performance variations (0.67% - 38.67%) and degraded performance among most agents, complex tasks generally benefit more from collaborative assistance ($4.6). Leveraging the dissimilar complexity levels of different source repositories, our repository analysis ($C.7) lends further evidence to the negative correlation between increased task complexity and recovery success.
(4) Resource Awareness and Efficiency. Our experiments with varying resource constraints ($4.7 & C.5) unveil critical insights with regard to L.M agents' resource awareness and adaptive resource utilization. While their underlying technical capabilities significantly affect their time resource utilization, LLM agents' recovery performance demonstrates their notably low awareness of both temporal and financial resources. As early-stage resource allocation proves crucial for attaining recovery success, strategic action planning and resource estimation are highlighted for sigh-performing and resource-efficient out-of-sync recoveries.
Broader Implications for Collaborative Systems. Our findings reveal meaningful implications for the future development of collaborative systems, especially in real-world scenarios with dynamic environments and intricate task contexts. From system design perspectives, the agent out-of-syRc challenge in real-world collaboration scenarios emphasizes the importance of state monitoring and divergence detection that are able to provide collaboration with effective recognition of the state mismatch B& # Sy taking place at time Tg ($2.1). The low ASR (< 4.86%) among all L.MM agents also underscores the value of stronger collaboration initiative and communication capabilities for effective collaboration. Our implementation of resource-aware out-of-sync -yyc recovery demonstrates the necessity of intelligent resource allocation and estimation strategies, illuminating both the importance of carly-stage investment in environmental understanding and the need for adaptive resource utilization based on task complexity and resource availability. In designing effective collaboration protocols, work elaborates the benefits of collaborator assistance, meanwhile highlighting the value of quality-focused rather than quantity-focused communication.
Limitations and Future Work. While our study provides meaningful insights, several limitations present potential directions for our future work:
(1) Benchmark Limitations. Although our benchmark construction method is applicable to diverse GitHub repositories and therefore can be further expanded to larger sizes to accommodate custom use (e.g., large-scale training), our SyncBench
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
($3) currently focuses primarily Gitflub Python repositories with unit tests, which may limit its generalizability to broader software engineering scenarios with dissimilar programming languages and testing environments. Additionally, our simulation of collaborative out-of-sync scenarios based on real-world historical repository changes may not fully capture all complexities of live collaboration patterns, especially in large-scale collaborative software engineering with multiple collaborators involved.
(2) Evaluation Framework Constraints. Implementing resource-aware out-of-sync recovery, our simplified resource modeling may not capture all real-world constraints involved in multifarious collaborative scenarios in reality. In our future work, we aim to include more diversified resource utilization with deeper exploration of collaborators' long-term collaborative relationships. We also endeavor extend our technical scope to multi-agent systems with multiple collaborators working on the task, resulting in more complex out-of-sync scenarios with multiple agents facing out-of-yync states (B,: + Sz).
(3) Methodological Considerations. There are also limitations in our methodological design. Our simulation-base approach may not capture all nuances real-world collaborations, and our investigation can be expanded to diverse communication protocols. To this end, our future work will also pay attention to the development of specialized training approaches to enhance agents' collaboration initiatives and communication capabilities, along with investigating more sophisticated resource management strategies that better mirror real-world development constraints.
B. Experiment Configuration
B.1. Pilot for Configuration
In determining the appropriate experiment settings, we conduct series of preliminary tests (Tab. BI) for basic interaction configurations.
Interaction Basics. We first pilot out-of-sync recovery on Llama-3. 1-708 and GPT-4o with the maximum number of turns limited 10 interactive iterations across 66 instances. For both agents, large proportion of tasks consist of 10 total turns of Env exploration without any solution attempts, while the remaining 11 tests have only one two solution proposal attempts without success. Based on this result, we further expand the max-turn limitation to 20 and 30 turns, respectively. Drawing upon the observation that fewer turns leave insufficient time for agents to propose their solutions, we finalize 30 turns of maximum interaction time to ensure the quality, effectiveness, and efficiency' of our experiments.
Resource Awareness. To define the proper cost setting of recovery actions, we compare the pilot experiment results of different cost settings in terms of initial budget (i.e., the total amount of money each agent given the very beginning of each test), polution-proposal cost (i.e., the cost of proposing solution for execution validation), and assistance-seeking cost (i.e., the cost of proactively asking for collaborator assistance). Setting the balanced cost of both aosistance-seeking as $100, we encourage agents to take these two recovery actions by providing them with an initial budget of $300, $1000, and $3000, respectively. Mcanwhile, all experiments are conducted with the maximum time limit set to 30 turns, as revealed by earlier pilot results. Comparing the performance between Budget = $1000 and Budger = $3000, deteriorated SR scores are obtained as we provide agents with adequate budget that can cover all kinds of action selection patterns. This unfolds LLM agents' lack of cost awareness that makes sufficient initial budget an obstacle towards recovery success. To this regard, we define initial budget of $1 $1000 for our standard aesource-wart out-of-sync recovery experiments, allowing agents to allocate freely up one-third of the maximum turns of interactions for solution-proposal and assistance-seeking actions.
and
Table B1, Pilot for Configuration. Preliminary tests to determine the appropriate experiment settings. All pilots are conducted through independent recovery, and refer to the number of colution-proponn attempts Artempts in this table to indicate whether current setting can support an effective out-of-syre recovery.
Time Limit (turn)
Budget (S)
Agent
Recovery
10
20
30
300
1000
3000
SR (%)
0.00
0.00
1.52
0.00
1.52
0.00
Llama-3.1-70B
Attempts
0
1
4
0
4
5
SR (%)
0.00
1.52
4.55
0.00
4.55
3.03
GPT-4o
Attempts
0
2
5
0
9
13

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Table Agent Out-of-Sync Benchmark. Our benchmark constructed through Env configuration, out-of-syne simulation, and multi- level filtering, and can be expanded to larger sizes for large-scale evaluation, or be further subsampled through weighted downsampling to obtain small-scale evaluation subsets.
Original Size
Reduced Size
Source
Callee
Caller
Total
Callee
Caller
Total
FastAPP/FastAPI
979
660
1639
4
10
14
huggingfacc//raasfoome
4635
453
5088
4
5
9
matplotih//mttlotii
757
980
1737
4
9
psf/requests
73
14
mwaskom/seaborn
356
217
573
13
19
pylint-dev/pylice
229
239
468
11
10
21
pytest-dew/pytest
1638
940
2578
8
10
18
openai/gym
53
89
142
16
sympy/sympy
887
1106
1993
13
10
23
pallcts/flask
34
266
300
4
10
14
openai/whine whisper
9
10
2
3
scikit-earn/sciki-leamm
376
2663
3039
1
6
sphinx-doc/sphine
675
434
1109
9
19
pycaret/pycaret
20
22
42
11
15
explosion/spaCy
313
434
747
10
21
python- python-pilloww/Plllos pillow/Pillow
295
1334
1629
11
10
21
scrapy/scrapy
465
665
1130
13
23
optuna/optuna
190
296
486
3
8
microsoft/FLAMM
38
30
68
4
3
7
psf/black
25
79
104
7
5
12
mlllow/mlllww
367
839
1206
3
8
Total
12711
11621
24332
150
150
300
B.2, Benchmark Construction
We design benchmark construction method $3 with generalizability and adaptability to diverse Python repositories that have Python as their primary programming language, meanwhile possessing unit tests for testing the functioning of various modules. Including environment setup (e.g., pyproject.toml, setup.py) is plus, while specifying necessary packages serves as an alternative way to enrich SyncBench ($3).
In the current version, our agent out-of-sync benchmark, SyncBench ($3), is built upon 21 popular Gütllub repositories (Tab. B2), and can be further expanded or downsampled to proper dataset sizes suitable for different experiment conditions and evaluation purposes.
Our benchmark construction method complies with our definition of agent out-of-syRc ($2.1), I), where meeting at least one of three conditions can result in an agent's belief deviation, either directly leading to task failure or indirectly laying latent problems for future execution errors. Although the latter outcome is not immediately visible, growing state divergences can be accumulated as underlying risks that lead to significant troubles the near future as the collaboration proceeds. To take all three conditions into account, our benchmark construction method supports both pass-to-fail and pass-to-pass samples, though we only consider pass-to-fail in our current version of SyncBench ($3.1) 1) to emphasize the importance of out-of-sync recovery abilities in maintaining effective collaborations.
Pass-to-fail state divergence. Out-of-sync scenarios directly visible pass-to-fail state divergence (B₂ * S2) are readily leveraged to construct out-of-yyne recovery tasks where: (I) collaborator updated repository (S,) can successfully pass the execution test, and (2) agent revised repository with out-of-syne function (B₂) fails the execution test.
Pass-to-pass state divergence. Out-of-sync scenarios that not manifested as immediate task failures also taken into consideration to create oul-of-sync recovery tasks based on pass-lo-pass state divergence (B₂ + S2) where: (1) collaborator updated repository (S1) successfully passes the execution test, resulting in parsed test output denoted as Os, (2) agent revised repository with out-of-syne function (B2) also successfully passes the execution test, resulting in parsed test output denoted as Og, and (3) parsed test output results between S₂ and B2 are different (Os + 0z).
As our determination of recovery success depends on the success of both execution test and parsing validation ($3.4), both
14

---

SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
pass-to-fail and pass-to-pass out-of-sync recovery tasks be effectively evaluated.
In experiments ($4), we test on 300 downsampled instances' with evenly distributed Caller and Callee ($3.2) for computational efficiency and comparison effectiveness.
Here are some examples of our test instances:
(1) Example-1 on psfirequests:
Traced git commit: c011122290006600-488860111111111
Out-of-sync function:
def _urllib__rquuett_oooe
request: "BreparedRequest",
verify: "bool str None"
"ityping.Dict [str, Anyl, typing. [str, typing. Any]) ) "
5
host_params = ()
pool_kwargs = ()
parsed_request__rr] urlparse (request. url)
scheme parsed_requsst_url.coo scheme. lower lower
port parsed_request_uri_.
10
cert_reqs "CERT_RRQUIRRD"
if verify is False:
cert_ "CERI "CEET_NONE"
NONE"
if isinstance (verify, str):
vool_wwargs"ca__ert
- verify
pool ("cer_rr"crrtrreepe" - cert_reqs
fost_params-
"scheme": scheme,
"host": jersedd_eeestt__.__ url
hostname, htw.sestter....stteeeeeee
"port": port,
return hest_params, kwargs
Initial error log:
test session starts ........ a
1inux Python 31.1.9, pytest-8.3.2, 1... h
cachedir: .pytest_cacci cache
rootdir: hortapacccsssttreeeeccaaaiiiiii
pyproject.tom!
plugins: httpbin-2.0.. cov-5.0.0, asyncio-0.24.0 asyncio: mode=Mode STRICT, default_loop_ssgeeton
collecting collected 329 items
PASSED
0&]
PASSED 0%]
hrtpapaceeeeccc........oaaoeeeen n:3128/1 PASSED
PASSED 08]
tests//testrrequeste py: htt.://ee.....t................a
[$]
testss/testrrequetts py: hrgp://wwee.aatotoooooooooooeeer
PASSED 18]
hrg.a///eeeettt.oe....o..eee..er PASSED PASSED 2$]
PASSED [ 18)
FASSED [ 2$]
tests/testtrrequette
PASSED
PAILED tests/test_requett sespeptssssstpyrr.eeeeessssssss
: Res :tatttt:::ttt___eee
15

---

SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
FAILED FAILED ortpapawe..aaaaaaiiiiooooooaaoaa
TypeE...
FAILED ottp:///eeecaaiiiaiiiiiaiaraaaar
FAILED ection org.apaaeeeaaaaaaaaa.aaaaaaaaaar
FAILED
FAILED FAILED http://wwe.ccaaaaaa.......aaaaaa FAILED http:///eeaccaaaaaaaaa.aaaaa.aa. (connect, read)
: :ypeErro::: Typ... -yp...
PATIED httpa//eeeeaaaaaaai..iaiaaaaaaar be an int, float
or None] hrtea//eeeeeeeiiieeerrrrrrrrerrr
PATLED FAILED hrg.ahac.....aaaaaa.a..r....aaer FAILED httpp///eeeccaaiaaaaaaraaraaaaaa Typ...
"ypekrr....
Typ...
FAILED http:///eeeeaaaaiaa.caaaaaaaaiar Typ...
FAILED ottp:///eeeeeaaaaa..o..o..ooooor FAILED http:///eeeeaaaaa..........ooaap FAILED org.aha.ee..aataaa............ar FAILED http:///we.ppaaaao...........ear FAILED nvaiidURL] FAILED FAILED org.apaeeeeaaaaaaaa.......aaaaar
FAILED haapg:/c...eaaaaaaaacacoaoooonaa FAILED http:://eeeeaaaaaaaacaa.aaaaaaan FAILED http:///eeeeaee...........a.aaa.
_
FAILED otg.ahaeee...g.n..........aaaaan
FAILED org.apaaeeeeeee.eaaaaaaa..aaaaar oecinng...utiisoonnddoooonnooore
FAILED
settings 131 failed, 196 passed, 1 skipped, xfailed, warnings in 30.10a
Ground-truth function:
def wurllibbbb...m ceaquss__contexx
request: "BreparedRequest""
verify: "boel I str None",
client_cert: Syping..Tulelstt. str] str None".
poolmanager: "PoolManager",
)
(ttyping.Dict [str, typing.Anyl, Syping.Dico [str, typlng.Any])
host_params = ()
pool_kwarge ()
parsed_request_un1 urlparse (request.url)
wtc.tttur..scceee.mee.ee.om.eeee scheme. ()wer port = reques_uu
scheme
11
Determine if have and should use our default sSI.onteet.
to optimize performance on standard requests. moolmanager_kwar
15
getattr (poclmanager, "comnection_pool_ee !1) has mas__poolmanagrr..mmm ssl ssl_context = poolmanager orrrraeeee__arrrrrreeesrssssoot
16
("sss__ooooeee""
should_ use cesee_defautt__eee context =
_pwelcadedd sal ssn_etttexttttttteeee is not None and has has_poonnangerr.ssccottete
20
cert_rcop =
if verify is False:
cert_ree pert_reqs "CERT NONE"
elif verify is True and shoull__ue_deffuttt_ss
16

---

SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
pool_ [as__wwargsssss_onteee" elif isinstance (verify, str):
-=relloadd__sl_conneex
26
if not cs us.ppth. isdir (verify):
peol_kwargs| ("caccrr"" = verify
else:
30
pool _eol_kwargs ("ca_ ""c__cert_de cert_reqs
dir"] ["ea_cert_dir"]
verify
31
if clicnt_cert is not None:
if asinstance (client_cert, tuple) and len (cllent__err) 2: pool_kwargs ""cer_ file"] client_cert pocl_kwargs((Mey__1ae
35
client_cert [1]
36
else:
According to our docs, allow users to speci just the cf cenn cert path
pool_kwarge file"] client_cert
40
host params =
scheme"; scheme,
"host"; parsed_reguest_urz hostname,
"port": port,
return host_paramss _wargs
(2) Example-2 on nwaskon/seaborn:
Traced git commit: 45506856aaaf447770005saaaaaaaaa3
Out-of-sync function:
def add
self,
mark: Mark,
stat:
Stat None,
data: Opticnal [DataFrame Mapping] None, Optional [str, Optional [Hashable Vector]]] None, orient: Literal["x", "y", "y", "b"] "x",
variables:
-> Plot:
layer_data self. dalff.ddta..coneeee (data, variables)
12
if stat is None:
stat = mark default_stat
orient ("v"; "x", "h": "e") (orient, orient)
16
mark.oriem. orient
if stat is not None: stat staent orient
self. layer_ append (Layer (layer_ data, mark, stat))
return self
Initial emrrr log:
test session sessesseee semossssppo
linux Python 3.11.9, pytest-8.3.2, pluggy-1.5.0 -- wworkspace/test. test__veer/bin/pytthoo cachedir: -pytest_cactt
rootdir: /norkspacc/test_rqpp
configfile: pyproject.
plugins: asyncic-0.24.0
asyncio: mode=Mode. STRICT, STRICC,, default__loppscope-
collecting ... collected 14 items
17

---

SyncMind:
Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
oaaaaa/maaaaaaoooo.....eee.e.a.. FAILED
7%]
ottpa///ae.aaaaoooo..oooooooaaar FAILED
148]
FAILED
21
[color) [color]
FAILED
288]
FAILED [ 358]
FAILED 428]
FAILED 50%]
57%]
FAILED
64%]
uege://aaaa..ooccccrrrrr...ee111 FAILED
71%]
788]
cete:///eeaa/errr00.....e.eeeeee | FALLED
858]
FAIL,D
928]
[atff///aaaasssacooo.r.r....a1ar unfilled mix FAILED
(1008]
FAILURES
restDet.test_sing..
self <tests._.arrkstettt.... TestDct object at 0x7f4196cb990
def test (est_simmle(self))
[1, 2, 31
y 14, 5, 21
P = Plct (x=x, y=y) .dd (Dot ()) .plot
tests/ bests/_marksttes_ddo..oo
self = <seaborn. core pww.::.....oeeo........... Plot object at 0x7f419997afd0>
mark = Det (artist (artist_kws=== marker=<'o'>, pointsize=<6>, stroke=<0. 75>, color=x!C0'>
alpha=<1>, fill=<True>, cdgeoler=<dpond:eoll odgewiåth=<0.55 odgestyle=<!-13)
edgealpha=<depend alphaa,
stat = Nonc, data None, variables = None, orient = 'x'
def ( self, mark: Mark,
stat: Stat None,
seabom//ccorepllt..yyyyoo
AttributeError
self = stetss_maakstest_dde.eeer object at 0x7E979214a990>
def test_ ledd__filled_mfilleee...
x = [1, 2] [4, 5]
marker ["a", "b"] shapes ["o", "x"]
mark - Bots
.plot TypeError: ald got unexpected keyword argument 'marker'
Plot (x=x, y=y) add (mark, marker=marker) sccale (marker=shapes) .plaat ()
cetts//mankk/csssdddd......1 Tpp.:
short test summary info AttributeBrror: 'plot...
FAILED http://acscccooe..........:aaaa.
FAILED http:///weettttta...o.ooortttttt
Typeärro...
FAILED http:///we/eeaaaaor...oooooo.oor
Attr
FAILED
FAILED hetpp///aeeaaaaaorrrrroo....aaar
FAILED
Xeger//aaeeggrlaoor.r..ooo.essae marker]
FAILED
18

---

SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
AtcributeBrror: 'Plo...
FAILED bet.h//att/eeeecooo......eee..s2
AttributeBrror:
FAILED FAILED http:://aaaacoor.....r..o..sss..
oetp://cccakaaaarrrrr.rrrr:r::rr - AttributeBrror: :
Attributerrror 'PlotD...
FAILED http::/aaaeceercdeerrreeeeeeesss
Stt.rppae.ttttttatttttttttteror AttributeBrroz:
FAILED fete://aaakcccrcc.eerrrrreeeeeee
AttributeBrroor 'Plo...
FAILED
TypeEzr...
failed in 1.16s 
Ground-truth function:
def add (
self,
mark: Mark,
stransforms: Stat Move, orient: str None None,
legend: bool True, label: str None None, data: DataScurce = None, .variables:: VariableSpee, Plot: "**
Specify layer of the visual ization in terms of mark and data transformss).
This is the main method for specifying how the data should be visualized. It can be called muitiple times with different arguments define plot with multiple layers.
16
Parameters
mark : :class: 'Mark'
The visual representation of the data to use in this layer. transforms :class: 'Stat' or :class: "Move'
Objects representing transforms to be applied before plotting the data.
Currently, at most one relass: 'Stat" can be used, and must be passed first. This constraint will be relaxed in the future. orient "x", "y", "v", or "h" The orientation of the mark, which also affects how transforms rypically corresponds The "v" (vertical) and "h" (horizontal) options are synonyms for "x" / "y", but may be more intuitive with some marks. When not provided, an orientation will be inferred from characteristics of the data and scales. legend bool
25
26
computed. the axis that defines groups for aggregation.
30
Option suppress the mark/mappings label : str
this layer from the legend.
34
35
A label to use for the layer in the legend, independent of any mappings. data DataFrame or dict
Data source to override the global source provided in the constructor. variables : data vectors or identifiers
39
Additional layer-specific variables, including variables that will be passed directly to the transforms without scaling.
Examples
44
include:: w./tbcstringg//bjeettt Plott add. aad.res rst
if not isinstance (mark, Mark)
48
mag f"mark must be a Mark instance, not (type (mark) !r)." raise TypeError (msg)
TODO This API for transforms was late decision, and previously Plot.add accepted or Stat instances and 0, 1, or a list Move instances.
52
19

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
It will some work to refactor internals so that Stat and Move are treated identically, and until then well need to "unpack" the transforms here and enforce limitations on the order / types.
stat: Opticnal [Stat]
move: Opticnal [Mist [Move]]
59
error False
if not transforms:
stat, meve None, None
elif isinstance (transsfomms [0], Stat):
stat transforms [0]
move = [m for in transforms if isinstance (n, Move)
error een (move) !- len (transforms)
else:
stat None
68
move = Im for m in transforms if isinstance (m, Move)
error = len (move) != len len (transforms)
if error:
72
msg join[I
"Transforms must have at rost one Stat type (in the first position),", "and all others must be Move type. Given transform tsp (str (type (t) nare for t in transforms) *."
1)
77
raise TypeError
new self._clone() new _ta.aaa.rrppeeddlllll..... ap
"mark": mark, "stat": stat, "move": move, TODO it docsn't work to supply scalars variables, but it should "vars": variables, "source"; data,
86
"legend": legend,
"label": label, *orient": ("v": "x", "h": get orient), type: ignore
1)
91
return new
B.3. Out-of-Sync Recovery on SyneBench
Employing resource-aware out-of-sync recovery framework (SyncMind $2), Fig. B1 presents agent out-of-sync recovery example our benchmark (SyneBench $3).
C. Agent Out-of-Sync Recovery: An In-Depth Analysis
As the continuation of our findings and discussions ($4), in this section, we aim to provide an in-depth analysis of agents' out-of-yync recovery efficiency, conditional recovery performance, along with their strategic recovery patterns terms of temporal dynamics of recovery actions, solution proposal strategies, effective assistance seeking, and resource awareness characteristics.
C.1. Conditional Recovery Performance
As complement exploration to agents' technical problem-solving capabilities conditioned on their localization success. By calculating CSR ($3.4), we extend our evaluation results (Tab. C1-1) to include conditional recovery analysis both agents' overall performance (Tab. C2) and their separate performance on Caller and Callee (Tab. C3).
previous observations with regard to agents' huge performance gaps ($4.2), expand
(1) Overall Performance. As summarized in Tab, C2, we integrate CSR with the overall evaluation of all seven agents (Tab. C1), which not only affords evidence different L.MM agents' significant technical ability gaps ($4.2), but further
20

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Context
Environment
Propose Solution
Ask Collaborator
To-Ta: Context
Bulance: 1000
2200
Balances 8 7000
helgftl
You georniingg Pytase tode Pyttou repostor "ept
Thnckapses prpp 6-] have bragpt susso
your solition proporny attempt outs
Josp.rooe_.........m text play,,, the file 'verispacs/tees wttp//tttttttttttttteeteeee http////tt/////////eeeee/ saalllsttsssppple
Bun whiee with poue-quettio eneenge anterge sott ausn
User "[[uddee: S100o]" Twiing lup feled a Silowed [-]
*slated are runctson cailing the other, theretore resutting in arrors or then toth!
FAILED losta willtte ploup
Jop, vod cas TyyeEnoroa [o]
Liece
"[Selance: yyom Lefl No, they not entattd to other tallng masther.
PAILE utile Laay stedd ralld, paore, wwamings
Tit: Enu Interaction
Balances $ 700
T3: Interaction
Balances 1000
Apcal
[un] filel /barkkapes//aat appp/tstt//ltiil//ee play.ps", 4, 100)
[Thought] start, will locasa the sest files und cxenins the contens of
in the file heee_play-ry These fuertaans are responetble the siTl no open the "cattppaay. blayyyy by file and ecsatar there functtone to undenstard the cause of tnvors.nnyater start by opreing the "test rest_play-ny rite.
ceas loep real, env" and "tast na Jaya functions underatand the caise of the arrons. First, let's search
test fanctions
testsjuttia/ diractory.
T4: Propose Solution
100
Balances
[...]
Propose Solution
Balance:
[silutten] vould like to propese solution by revising the falled furctions
test locp ow and test no keys in playioy
Agunt
Arsolate pobl: /warkapaecl bost rawp/ssata /ataaaaaaaaaaaaaaaaaoaa best pl
[Boution] ener doe uosted play play.sy
Jwp
Atsolace path: Nenispacc/itest www.s/ottiissssl...o...
Absolate seth: /varkopwoo/ wtwp///oouuuuiio rew/ooato http://///////////ootttto// pll
play
Function rewer sest re, laya
[cool sill update
accordingly.
"play".
der test (n:
accunions, kayz, www.bt.actionnions
s2ED
"'artPote-x3" [ood]
play (eSsstrgeesteeettiin)
tleck,sick(fau)
play(gye.nek nakel Sart)sle-s11")
repodbrr' 'tw__rpp' axsnefilly posed
ResourceeAuuareeuuoff-- Recovery
Incorrect Solution
Coluuii
Figure BI. Out-of-Sync Recovery Example. Starting from the initial out-of-sync state at 12 LLM agent takes different recovery actions to approach synchronized state through multi-turn interactions, which involve interacting with Eny (green blocks), proactively asking for collaborator assistance (blue block), proposing solution examine agent's recovery progress (orange blocks). By incorporating resource awareness throughout the out-of-sync recovery process, the agent pays extra heed to making rational use of available resources for gaining knowledge and validating solution cost. For better clarity, some details are omitted [...].
Table C1. Overall Out-of-Sync Recovery Performance. We use Acalinborator to represent the influence of collaborator assistance.
Independent (%)
Collaborative (%)
Acollaborator (%)
Agent
LAfae
LAjune
SR
LAfste
LAfune
SR
LApate
LA
SR
Ilama-3.1-88
8.67
4.67
0.33
27.00
21.33
1.33
+18.33
+ +16.66
+1.00
Llama-3.1-70B
8.33
5.00
2.67
12.33
5.67
3.33
+4.00
+0.67
+0.66
GPT-4o mini
10.63
7.97
3.99
12.29
8.31
5.32
+1.66
+0.34
+1.33
DeepSeek-V2.5
47.67
35.00
7.33
47.00
37.33
7.67
-0.67
+2.33
+0.34
GPT--4
14.33
9.33
4.00
39.00
34.67
8.00
+24.67
+25.34
+4.00
L.lmm-3.3-70B
64.00
47.33
16.33
66.67
53.67
19.00
+2,67
+6.34
+2.67
Claude-3.5-Sonnet
64.09
56.35
28.18
61.33
51.93
33.70
-2.76
-4.42
+5.52
Table C2. Conditional Out-of-Sync Recovery Performance. Following Tab. C1, A Acaliabenator represents the influence of collahorator
assistance.
Agent
Independent (%)
Collaborative (%)
Acallatorator
(%)
CSS CS
une
SR
CSR CSR
SR
CSR CSR
R
Llama-3.1-8B
3.81
7.07
0.33
4.93
6.24
1.33
+1.12
-0.83
+1.00
Llama-3.1-70B
32.05
53.40
2,67
27.01
58.73
3.33
-5.04
+5.33
+0.66
GPTT-4 mini
37.54
50.06
3.99
43.29
64.02
5.32
+5.75
+13,96
+1.33
DeepSeek
15.38
20.94
7.33
16.32
20.55
7.67
+0.94
-0.39
+0.34
GPT-4o
27.91
42.87
4.00
20.51
23.07
8.00
-7.40
-19.80
+4.00
Llama-3.3-70B
25.52
34.50
16.33
28.50
35.40
19.00
+2.98
+0.90
+2.67
Claude-3.5-Sonnet
43.97
50.01
28.18
54.95
64.90
33.70
+10.98
+14.89
+5.52
contributes to the finding that strong technical programming capabilities alone is insufficient for effectively maintaining
21

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Table Conditional Out-of-Syne Recovery Evaluation on Caller and Callee. The influence of increased task complexity introduced by dependency tracing on agents' conditional out-of oyne recovery performance: Dowpiesit
Calliv-CCller)
Caller (%)
Callee (%)
Ccmppissity
%
Agent
Recovery
C
SR
CSR
SR
CSR fite CSRfune
SR
Independent
9.98
16.63
1.33
16.75
50.38
0.67
+6.77
+33.75
-0.66
Llama-3.1-BB
Collaborative
6.25
7.69
2.00
3.05
4.02
0.67
-3.20
-3.67
-1.33
Acollaborator
-3.73
-8.94
+0.67
-13.70
46.36
+0.00
-9.97
-37.42
-0.67
Independent
46.14
75.05
4.00
16.63
28.48
1.33
-29.51
-46.57
-2.67
Llama-3.1-70B
Collaborative
27.75
55.50
3.33
26.28
62.48
3.33
-1.47
+6.98
+0.00
Acollaborator
-18.39
-19.55
-0.67
+9.65
+34.00
+2.00
+28.04
+53.55
+2.67
Independent
40.03
57.20
5.32
33.38
40.06
2.66
-6.65
-17.14
-2.66
GPT-4o mini
Collaborative
52.16
66.64
7.97
28.60
57.20
2.66
-23.56
-9.44
-5.31
Avoilaborator
+12.13
+9.44
+2.65
-4.78
+17.14
+0.00
11699
+7.70
-2.55
Independent
14.95
18.32
8.67
16.07
26.47
6.00
+1.12
+8.15
-2.67
DeepSeek
Collaborative
16.67
18.32
8.67
15.88
24.41
6.67
-0.79
+6.09
-2.00
coollbborator
+1.72
+0.00
+0.00
-0.19
-2.06
+0.67
-1.91
-2.06
+0.67
Independent
45.47
58.87
6.67
9.50
18.14
1.33
-35.97
40.73
-5.34
GPT-4o
Collaborative
25.43
28.30
10.00
15.52
17.65
6.00
-9.91
-10.65
-4.00
coolloborator
-20.04
-30.57
+3.33
+6.02
-0.49
+4.67
+26.06
+30.08
+1.34
Independent
23.14
31,12
18.67
29.58
40.38
14.00
+6.44
+9.26
-4.67
Llama-3.3-70B
Collaborative
28.45
34.02
22.00
28.57
37.50
16.00
-6.45
-5.45
-6.00
colllaborator
+5.31
+2.90
+3.33
-1.01
-2.88
+2.00
- 12.89
-14.71
-1.33
Independent
49.99
53,48
25.41
40.00
47.46
30,94
+9.99
-6.02
+5.53
Claude-3.5-Sonnet
Collaborative
66.67
74,30
28.73
48.55
59.32
38.67
-18. 12
-14.98
+9.94
collaborator
+16.68
+20.82
+3.32
+8.55
+11.86
+7.73
-28.11
-8.96
+4.41
synchronization in collaborative software engineering ($4.3).
Persistent Gaps In LLM Agents' Technical Problem-Solving Capabilities. As one of the high-performing agents, Claude- 3.5-Sonnet showcases its robust problem-solving capacity in conditional recovery (CSR 2 43.97% CSR 2 50.01%), with positive gains from collaborator assistance in both CSR (Acollaborator = +10998% and CSRfanc (Acollaborator 14.89%). This effectively substantiates the importance of both strong technical efficiency and effective collaboration in successful out-of-sync recoveries. On the other hand, /lama-3. 1-8B consistently exhibits limited technical recovery capacity at both file (CSRfite 4.93%) and function (CSRffu une < 7.07%) levels. The huge technical recovery gaps in both CS and CSR complement our prior observations on LLM agents' persistent ability gaps ($4.2).
Technical Proficiency Alone Is Insufficient for Recovery Success. Our calculation of agents' CSR scores (Tab. C2) also corroborates that strong technical SE capacity alone is insufficient for out-of-sync recovery success. For example, showcasing comparably high CSR scores at both file and function levels, GPT-4o mini and (..ma-3311-77 obtain their success rates less than 5.32% and 3.33% (Tab. CI), respectively. Their significant lack of willingness to collaborate (Llama- 3. 1-708 with ASR 1.37%, GPT-40 mini with ASR 1.69%) (Fig. 7), combined with their low localization accuracy (LA fite < 12.33%, LA < 8.31%) (Tab. CI), further demonstrates the significance of multifaceted capabilities for obtaining out-of out-of-syne recovery success ($4.3).
(2) Caller versus Callee. In assessing agents' conditional performance separately on Caller and Callee, we summarize Tab. C3 by incorporating CSR calculation into Tab.
Expanding our prior analysis ($4.2) to technical recovery capacity, agents' ability gaps remain huge in conditional recovery success both file (from Llama-3. 1-8B with CSRfue 3.05% to -eaude-3.5--onned with CSRffte 66.67%) and function (from 1lama-3.1-88 with CSRfune = 4.02% to Siaude-3.5-oomee with CSRfune 74.30%) levels, with in general diminished performance on Callee. Nevertheless, Claude-3.55 3.5-ee-----ooee continues to excel in delivering superior conditioned technical recovery capabilities (CSR 2 40.00% and CSRfume 2 47.46%)
On the other hand, the comparable technical recovery capacities among Claude-3.5 3.5-de--.5-Somaee S-Sonet, GPT-4o mini, and GPT-4o mini, as demonstrated by their recovery performance on both Caller and Callee, resonate strongly with their overall performance
22

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
(Tab. C2), substantiating the insufficient role of technical proficiency in effective out-of-sync recoveries ($4.3).
First-half Failure Action Distribution (%) First-half Success Action Distribution (%)
Recovery Setting
Action
Collborative
independent
Assistance Seeking fata (ASR)
Soluzien Proposal Rate
Erv interaction Rate
100.00
95. ww..2000.0g... 00
84.82 58.24
100.00
79.67
85:71
93.62
79.03
13
64.98
50
25-
(a)
0.00
0.00
0.00
0.00
0.00
0.00
0.00
50
75-
95.24
100.00
88.68
75.00
72.41
85.95
100.00
80.95
86.01
Llama-3.1-88
Llama-3.1-708
Llama-3.3-708
GPT-4o mini
GPT-4o
DeepSeek
Claude-3.5 Sonnet
Recovery Setting
Collbbraaivve
Independent
Acsistance Sooking Rata (ASR)
Solutin Proposal Rate
Erv intercctio Rate
1.0
03.37
91.93
07.57
79.7279.10
79.10
23
54.70
02.16
65.36
50.39
65:68
55.76
25
(b)
0.00
0.00
0.00
0.00
0.00
0.00
0.00
50
75
57.83
60.68
50.06
Llama-3,1-88
Llama-3.1-708
Llama-3.3-708
GPT-4o mini
GPT-4o
DeepSeek
Claude-3.5 Sonnet
Figure CI. First-Half Action Distribution in Success and Failure Cases. We visualize the average action distribution of each LLM agent their halt- recovery turn.. For each agent, Figure depicts the average proportion of each action taken during the first half of its successful recoveries, while Figure (b) illustrates these proportions for its failure cases.
C.2. Temporal Dynamies of Recovery Actions
Our discussion collaboration effectiveness ($4.5) uncovers the consistent patterns of advancing repository exploration for out-of-syne recovery success (Fig. C1). The comparison action distribution in the first half of recovery time between (a) success cases and (b) failure cases presents the positive correlation between effective communication and successful out-of-sync recovery. By extending their action distribution to concrete indexing, we visualize LLM agents' time allocation throughout their out-of-syne recoveries (Fig. C2), which sheds light on the effective recovery strategies with regard to agents' action taking patterns and temporal dynamics.
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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
L.3-0-33-700: Independent Agent: SR CA LAxe
L.mm-3.1708:2..77 2.67% 8..3%% | 15.00%
Mini 3.00% [:0.63%,] 7.07%
DespSaet-V2S5 7.33% (4.67% 135.00%
Cludee33.5 Sonnett
DeepSeee--2255
GPT-to
GPT-4g mini
Uama 3.2 709
Uama-3
Collaborative Agant: [Arr.
Llam--3 1-80: 1.33% 121100% 33.33%
Unmm-3.3--P 3-3700 19 10.00% 100.8 (3.88%)) 53.6.%
GPI-tur (0%% 19 Joros: 94.77%
Cleude-J.---amee 33.70% 60.33% 51.975
Liama 3.1-708 3.33% 12.33% 5.61%
GPI-4o-in:: 5. 32% 32.20% 8.31%
DeepSeak V2.5: 7.67% 47 00% 37.33%
HiPS: 0.49
IxPS: 8:93
IIPS: 5.06
IIPS: 14.45
IIPS: 34.57
IiPS: 17:32
HIPS: 17.40
Claude Sonnet
DeepSesk-V2255
GPT-40
(b)
Ulama 3-700
Llam--11-700 3:1700
Lama-3.1-88
MAC:
MAC: 4.22
MAC: 6.07
6.09
mAC: 10.93
12:14
32:45
Ask collaboratir assisance (4C)
Pispose recovery solution IP5)
Figure C2. Recovery Time Allocation. Through (a) independent or (b) collaborative out-of-sync recovery, agents distribute their recovery actions freely different tums. As suggested by figure legends, actions of proactively asking for collaborator assistance (AC) are denoted as arrowheads heading to the right (D), while actions of proposing a recovery solution (PS) are represented as arrowheads heading towards the left (4). For each agent, also calculate the mean index of its AC (mAC) and PS (mnPS), indicated dotted lines (-....) and dashed lines (- --- -)_ respectively. Employing the shade of colors to indicate the frequency distribution of actions taken at each turn, the more recovery actions turn index assigned, the deeper the color the turn index is superimposed.
Exploring earlier and solving later. Choices of actions across all recovery turns are arranged in Fig. C2 according to their corresponding indices, where overlapped selections create a gradient of color depth to reveal the actual interaction allocation of each LLM agent in the course of recovering its belief state the up-to-date world state. Fig. C2 therefore elaborates the time allocation patterns of different agents under the standard 30-turn out-of-sync recovery setting through (a) independent or (b) collaborative recovery ($2.2). Leveraging action indices, we calculate. for each LLM agent, the mean index of its proposing solution (mPS) or proactively asking for collaborator assistance (mAC) actions. Comparing the performance of different LLM agents, both independent and collaborative recoveries showcase the positive correlation between later-turn solution proposal and
recovery (e.g., Klaude-3.5-oomee and Llama-3.3-700 with their mPS 2 15.28 and SR 16.36 1..3%.). Comparing mPS with mAC, collaborative agents by and large seek collaborator assistance in markedly earlier turns (mAC e [3.35, 12.45)) while deferring solution proposal to some later time (mPS e [8.49, 17. 17.40)) which resonates strongly with first-half action distribution patterns between successful and failed recovery cases (Fig. CI). Viewing (a) independent and (b) collaborative recovery collectively, agents with higher performance in general defer their solution proposal actions to some later turns (mPS > 14.87) after exploring the codebase in the first half of time through interacting with En or proactively seeking collaborator assistance (Fig. CI). Integrated with allocating a major proportion of interactions to Eny exploration, agents are able to better their performance through appropriately postponing their choices of solution proposal until obtaining sufficient contextual knowledge to establish synchronized mental models of updated states for effective solution proposal attempt.
24

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
C.3. Solution Proposal Dynamics
Our experiments reveal a 'Goldilocks zone' for LLM agents' solution proposals- bott excessive and insufficient proposal attempts correlate positively with reduced recovery success.
Low solution proposal frequency is as adverse as frequent solution proposal attempts. Possessing the least awareness of proposing a solution towards out-of-sync recovery success, GPT-4o mini (Fig, 7) presents only 1.30% time for solution proposal during its independent recovery, which is on average 0.39 turn out of the total 30 turns allowed. Similarly, collaborative GPT-4o mini agent allocates merely 1.32% recovery time to propose a solution in its 30-turn recovery tasks. GPT-4o mini's low-frequency solution proposal patterns, which are only between [0.39, 0.40) turns on average, results in its noticeably underperformed recoveries (Independent: SR 4.00%, Collaborative: SR 5.33%), especially compared with other LLM agents with their solution proposal attempts above 6.00% recovery time. On the other hand, excessive solution proposals introduce analogous negative influence on agents' recovery performance. Llama-3. 1-8B allocates 31.32% recovery time in its independent out-of-syne recovery scenarios for solution proposal actions, while having 30.70% of its total time for proposing solutions in its collaborative recovery tasks. Despite its frequent solution proposal attempts, limited solutions (Independent: SR- = 0.33%, Collaborative: SR- = 1.33%) are validated to be effective for achieving recovery success. This impact stays applicable to other agents. For instance, Llama-3. 3-70B with 29.96% and 29.09% solution proposal in its independent and collaborative recoveries, respectively, achieves Independent-SLe Collaborative-STR 3.33%. Nevertheless, the timing of solution proposal attempts during later turns (Fig. C2) after gaining better understanding of the codebase may supplement further advantages in fulfilling successful out-of-sync recoveries.
2.67% and
C.4. Effective Assistance Seeking
According to task completion successfulness, we categorize agents' proactive questions into high-quality and low-quality queries ($4.5). By investigating the <query, response>> pairs collected from collaborative interactions between agents and collaborators, we summarize typical characteristics of two query classes in Tab. C4.
Communication- Perffrmmneeee Gap. Viewing separately LLM agents' communication performance and recovery effec- tiveness (4,44,,5) our findings reveal a notable disparity between agents' programming performance and communication capabilities- An LLM agent can be good programmer but a poor communicator. This can be clearly substantiated by the fact that mini achieves significantly superior upper-bound recovery performance (SR = 86.33%) with full collaborator support (64.4), especially considering its markedly lower performance (SR 5.32%) with ASR 1.69% in multi-turn recovery experiments.
showcases the best performance (SR 33.70%) with ASR 4.86% while GPT-4o
Improvement Implications. Fig. 8 unveils the significant advantage of high-quality assistance seeking, as revealed by high- performing LLM agents with a higher proportion of high-quality questions both query categories (e.g., Claude-3.5-Sonnee SR 33.70% with 21 21.35% high-quality localization queries and 16.00% high-quality solution queries). Nevertheless, arranged based on ASR from low high along the X-axis (Fig. 8), higher ASR scores present no direct correlation with larger proportions of high-quality questions in either category. This further substantiates the importance of enhancing question quality, rather than increasing question volume, for L.-M-based agents to achieve improved out-of-sync recovery performance. In pursuit of effective collaborator assistance ($4.5), we conclude question quality indicators in three aspects (Tab. C4): (1) Specificity, where high-performing queries precisely identify ambiguous areas, (2) Timing, where effective questions follow a logical progression from localization to solution refinement, and (3) Context Integration, where superior queries raise task-specific confusions pivotal to out-of-syne recovery success.
Here are some assistance asking examples of different LLM agents during their journeys of out-of-sync recovery.
Examples of low-quality queries:
(1) Vague queries:
TURN 24
AGENT:
Can you please help confirm if the function 1 init__ is out-of-syne?
HUMAN:
[Balance: $300 Left] I'm sorry that I'n not sure which : init you are asking for confirmaticn.
25

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
(2) Irrelevant queries:
TURN 2
AGENT:
Can you provide more information about the doctest items that are expected to be collected Speci ficill, what are the contents of the doctest itens, and what is the expected behavior of the doctest?
in the test case
HUMAN:
Balance: $900 Left| The test case "test_collect collect_module_ tww__ttww______ee__ee______eeeee doc_ooottttttteeeeeeeeeee no modulelevel" is creating a Python file with multiple functions, some of which contain string comments that represent doctests. The expected behavior is to collect these doctest items:
The string "\">>> magic
\ inside the "my_func" function, which is an incomplete
doctest item.
2. The string "\">>> import os & this one does have doctest\ "* inside the "another" functicn, representing an actual doctest.
The test is expecting these doctests be recognized and collected, thus expecting total two doctest items to be collected.
(3) Redundant queries:
TURN 2
AGENT:
Could you please help me locate the Python repository "test_repp and provide its absolute path? It seems cannot find it in the current working directory.
HUMAN:
[Balance: $900 Left) /workspacc/test_rggo
(4) Repetitive queries:
AGENT:
Where is the file containing the responsible code, and which function is out-of-sync? Specifically, can you please help provide the relative path of the responsible code?
HUMAN:
[Balance: $900 Left] relative path is /ttssts/tttlll/ttttt......
The out-of-sync function is "_get_videosiie"" TURN 4
AGENT:
What is the absclute path of the file containing the video size" method? Please provide the necessary information so can proceed with the task.
HUMAN:
[Balance:
$800 Left The assolute path of the file containing the "_get_ vee_ovide__sae method is htt.://ceeeessrrppptttttttttttss
(5) Over-specific queries:
TURN 6
AGENT:
Could you please help locate the source code files in the test_repo? need find where the requests module implementation located, particularly the file that handles URL preparation and request handling.
HUMAN:
[Balance: $800 Left| The requests medule implementation is Located in the 'test repo" directory under the path /twpkk/paceetettreeeeee httpss/eeeeeesrrrrrresss//////a ptt.:eettss/aapttr..pppppp where preparation and request handling are addressed.
(6) Speculative queries:
TURN 1.
AGENT:
26

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
think adding the key "by_msg" - "any_failoon Can you please help confirm whether this may be potential solution?
can be a solution to recover the function
HUMAN:
[Balance:
$700 Left] Yes, it is pessible solution.
Examples of high-quality queries:
(1) Explicit queries:
TURN
AGENT:
Looking at the error trace and code, I see that the error occurs in provider' method when trying add arguments the parser. Could the provider (which should a BaseChecker instance) is expected to be a list/tuple of (opt, optdict) pairs? This would help me understand if the error is due to incorrect data structure or missing arguments.
you confirm if the "options" attribute
HUMAN:
Yes, the "optiens" attribute of the provider is expected to be list tuple of (opt, optdict) pairs.
(2) Targeted queries:
TURN 3
AGENT:
Looking at the test file, see that the tests are failing with AttributeSrror. Could please share the specific AttributeError message from of the failing tests? The error legs you provided only show \"Attribute::ror: type obj... but are truncated. The complete error message would help me identify which attribute is missing.
HUMAN:
[Balance:
$900 Left) AttributeError: type object ''Node' has attribute 'append'
(3) Constructive queries:
TURN 18
AGENT:
In the failing test case
what is the exact value and type of "node. rect filee when the error occurs? you show me the value of the tuple or provide more details about structure?
HUMAN:
[Balance: $500 Left] "node. root () file"" is expected to be string, but when the error occurs, it is actually tuple. The exact structure of the tuple might not be fully visible in the error message, but it likely contains multiple elements that should have been a single file path string.
(4) Action-oriented queries:
TURN 17
AGENT:
Could you help me locate the "get_data_path function in the spacy codebase? And could you please help confirm if need import dependencies for this function?
HUMAN:
[Balance: $700 Left] The "get_ data path" function is located in the "spacy.util" module. But you don't need to import other dependencies. You only need to accommodate the new imports from "spacy.util",
(5) Instrnetion-oriented queries:
TURN 2
AGENT:
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Based on the error messages showing \"ypperrror:
in NER-related tests, - could you tell me if there's s a specific Lookups class or method that's a being called in these failing tests, and what is the expected behavior of this Lookups functionality that I should aim to implement?
HUMAN:
[Balance: $800 Left] There is a specific
method in the Lookups class that being called in these failing tests. The expected implementation of this mothod is to retrieve a table by its name and potentially use default value if the table doesn 't exist.
Table C4. Agent Query Characteristies. The summarization of agents' clarifying questions in their pursuits of out-of-sync recovery success.
QUALITY
CHARACTERISTICS
Vague Queries: Queries that unclear. lack explicit details, expressed in confusing manner, reducing the effectiveness of human responses in assisting agent's out-of-syne recovery.
Irrelevant Queries: Queries that are unrelated to the current context, task, or solution, providing scareely useful information for addressing agent's 's immediate confusion or progressing toward a solution.
Redundant Queries: Queries that seek information already provided or covered, hardly contributing to new value or progress. These queries may stem from failure recognize process previously shared information. Repetitive Queriex: Queries that are asked multiple times, often in identical or slightly rephrased forms, without any significant change in context, which can result in unnecessary duplication and inefficiency. Over-Specific Queries: Queries that unnecessarily detailed or human responses distracted from the main problem or delay the recovery progress by complicaigg agent's current confusions.
Low
on minor aspects, which leads to
Speculative Queries: Queries based on agent's assumptions, guesses, or hypothetical situations that fail to align with current task, potentially leading to confusion or ineffective human assistance.
Explicit Queries: Queries that are well-structured, unambiguous, and provide the exact information needed to articulate the question or confusion clearly, allowing human collaborators to quickly understand and effectively respond.
Targeted Queries: Queries that specific and target agent's immediate confusion or objective, ensuring human's 's respouse addresses the key problem without unnecessary distractions.
Constructive Queries: Queries that build upon prior information, human responses, and/or recovery failures, progressively narrowing down the essential recovery direction or advancing the recovery progress toward resolution.
HIGH
Criten--rieettd Queries: Queries that focus on actionable solutions or next steps, helping drive the out-of-sync recovery process forward effectively. Instruction-Orieen Queries: Queries that can effectively seek human instructions on generating a viable solution towards out-of-syne recovery success.
C.5. Resource Awareness
Endowing agents with the awareness of resource constraints and the ability for adaptive resource deployment is crucial for real-world collaborations. Implementing resource-aware agent out-of-sync recovery ($4.7), our investigation reveals signifi- cant limitations in agents' resource management capabilities across multiple dimensions, suggesting future improvement directions for resource-efficient collaborations.
Time Awareness. Comparing agents' time allocation (Fig, C3) and overall performance (Fig. C4) between the maximum 30 and 50 available turns discloses complex patterns in agents' temporal resource utilization. Contrary to intuitive expectations, extending available recovery time provides no guarantee for performance enhancement (Tab. CS), as increasing the maximum time limit from 30 50 turns shows diminishing returns on Llama-3. 3.1 3.1--B --B's success rates (Independent: -0.33%, Collaborative: -1.00%) while significant improvements on Llamo's. SR scores (Independent: +3.67%, Collaborative: +4.67%). The effectiveness of out-of-sync recovery appears more dependent on LLM agents' technical capabilities and strategic time allocation than total available time. This can be substantiated by agents like Ilumu-3. J- 70B (Independent: 88.68% Env interaction; Collaborative: 100.00% assistance asking and 94.64% Env interaction) that strategically concentrate exploration in early stages (the first half of time) while postponing their solution proposal to later phases. Claude-3.5 3.5- (SR = (3.70% also supports this observation through allocating 93.62% assistance seeking turns in its first half of recovery time among success cases. This emphasis on early-stage exploration proves more critical
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Table C5. Resource-Aware Out-of-Sync Recovery: Time Awareness. Performance summarization of resource-awant out-of- of--c
recovery. Unstated resource settings: (1) initial budget: $1000, (2) the cost of proposing solution: $100, (3) the cost of seeking
collahorator assistance: $100.
Time Limit: 30 Turn (%)
Time Limit: 50 Turn (%)
A (%)
Agent
Recovery
file
func
SR
file
func
SR
file
func
SR
Independent
8.67
4.67
0.33
8.67
7.00
0.67
+0.00
42.33
+0.34
Llama-3.1-8B
Collaborative
27.00
21.33
1.33
25.33
20.33
0.33
-1.67
-1.00
1.00
eallubwrator
+18.33
+16.66
+1.00
+16.66
+16.33
-0.34
-1.66
-0.33
-1.34
Independent
8.33
5.00
2.67
28.00
18.00
7.33
+19.67
+13.00
+4.66
Llama-3.1-70B
Collaborative
12.33
5.67
3.33
29.67
20.00
7.00
+17.34
+14.33
+3.67
Acoluborator
+4.00
+0.67
+2.33
+1.67
+2.00
-0.33
-2.33
+1.33
-2.66
Table C6. Resource-Aware Out-of-Sync Recovery: Budget Awareness. Performance summarization of resource- out-of-sync recovery with varying initial budget settings. Unstated resource settings: (1) maximum time limit: $30 turns, (2) the cost of proposing a
solution: $100, (3) the cost of seeking collaborator assistance: $100.
Budget: 1000 (%)
Budget: $ 3000 (%)
! Budger (%)
Agent
Recovery
file
func
SR
file
func
SR
file
func
SR
Independent
8.67
4.67
0.33
13.00
7.67
0.67
+4.33
+3.00
+0.34
Llama-3.1-8B
Collaborative
27.00
21.33
1.33
37.33
28.33
0.67
+10.33
+7.00
-0.66
Acullaurator
+18.33
+16.66
+1.00
+24.33
+20.66
+0.00
+6.00
44.00
-1.33
Independent
8.33
5.00
2.67
25.33
15.67
4.00
+17.00
+10.67
+1.33
Llama-3.1-70B
Collaborative
12.33
5.67
3.33
29.33
19.67
5.00
+17.00
+14.00
+1.67
collaburator
+4.00
+0.67
+2.33
+4.00
+4.00
+1.00
+0.00
+3.33
-1.33
Table C7. Resource-Aware Out-of-Sync Recovery: Action Cost. Performance summarization of resource- out-of-sync recovery
with varying assistance-seeking cost settings. Unstated resource settings: (1) maximum time limit: $30 turns, (2) initial budger: $1000, (3)
the cost of proposing solution: $100
Asking Cost: $ 50 (%)
Asking Cost: 100 (%)
Asking Cost: $ 200 (%)
Agent
Recovery
file
func
SR
file
func
SR
file
func
SR
Collaborative
29.67
22.00
0.33
27.00
21.33
1.33
24.33
19.00
0.33
Llama-3.1-8B
Acallsburator
+21.00
+17.33
+0.00
+18.33
+16.66
+1.00
+15.66
+14.33
+0.00
Collaborative
19.00
9.67
1.33
12.33
5.67
3.33
17.00
10.33
4.33
Llama-3.1-708
Acullaburator
+10.67
+4.67
-1.34
+4.00
+0.67
+2.33
+8.67
+5.33
+1.66
than the mere extension available time. In light of time allocation for different actions, extended recovery time notably encourages agents' Env interaction choices with reduced proactive assistance seeking and solution proposal (Fig. C3). The negligible performance impact of this shift highlights both the importance of adaptive action distribution and agents' limitations in optimizing extended time usage for knowledge acquisition and recovery planning. The consistent impairing effects of increased time availability on udvantageous collaborator assistance further underlines the significance of adaptive and strategic action planning extended recovery journeys.
Cost Sensitivity. Our investigation of budget-cost financial awareness reveals surprisingly low cost sensitivity among agents. Varying initial budgets between $1000 and $3000 (Tab. C6 & Fig. C5), where $3000 enables limitless action taking across 30 turns of recovery, produces minimal performance differences (s + +1.67% on SR). suggesting agents' scarce sensitivity to effective financial management. Similarly, halving or doubling the cost of proactively seeking collaborator assistance ($50, $100, $200) results in trivial changes to both assistance-seeking behaviors (time allocation differences 0. 0.26%, Fig. C7) and overall recovery performance (< 2.00% on SR, Tab. C7 and Fig. C5). This consistent cost insensitivity among LLM agents indicates fundamental limitations their abilities to strategically estimate and adaptively plan resource utilization.
Strategic Action Planning and Resource Efficiency, The sequence and timing of recovery actions significantly influence
29

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Recovery Setting
Action
Collaborative
Seeking
Proposal
81.27
75
Time Allocation (%)
50
25
13.88
2.20
0-
0.00
18:85
15.89
31.32
29.96
-30
70.04
81.15
-100
(Max 30)
[Max 50)
(Max 30)
(Max
Figure C3. Effects of Time Increment Time Allocation, As supplement Fig. 7, this figure further illustrates the time allocation changes of LLM agents when provided with more turns of interaction allowed.
+18.33%
Independent Collaborative
+4.00%%
Independent Collaborative
12.33
+16.66%
Llama-3.1 3.1-8
21.33
Llama-3.1-700
+0.67%
5.67
+0.66%
5.00
8.67
+0.33%
4.67
1.001.33
2.67
LArie
LAruc
LAgte (%)
LAnuc (%)
SR (%)
+16.66%
+1.67%
Independent Collaborative
Independent Collaborative
25.33
+13.33%
29.67
Llama- 3-1-8
20.33
1lama-3.1-708
28.00
+2.00%
20.00
(b)
-0.33%
8.67
7,00
7.00
-0.44%
7.33
0.67 0.33
LAnie
LArun
LAste (%6)
LArune (%)
SR (%)
Figure C4. Effects of Time Inerement on Overall Performance. As supplement to Fig. 7, this figure further elaborates the effects of increased available time the recovery performance of 1lama-3.1-8P and 1--ma-3.1--0B- the maximum time limit is to 30 turns, (b) the maximum time limit is extended to 50 turn..
30

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Initial Budget Solution Proposal
$10n0 -$100
Assintance Seeking -$100 -$100
Liama-3.1-0 3.1-88
Solution Proposal
Umme-3.1-70
Independent Collaborati
Performance (%)
Figure C5. Effects of Financial Variation on Overall Performance. In exploring the financial awareness agents, out-of-sync Recovery performance of different financial settings is visualized above with the changing initial budget or assistance seeking (AS) cost.
Recovery Setting
Action
Independent
Assistance Seeking (ASR)
Propesal
75
06:11
Time Allocation (%)
50
50.40
47.17
31.47
25
2.43
31.32
29.96
-50
49.16
68:68
65.52
70:04
-100
Llama-3.1-8B (Budget $3000)
Llama-3.1-708 (Budget $1000)
Llama-3.1-708 (Budget $3000)
(Budget $1000)
Figure C6. Effects of Budget Sufficiency on Time Allocation. This figure visualizes the influence of budget variations L.IM agents' time allocation changes.
Recovery Setting
Action Solution
Collaborative
Independent
Assistance
Intoraction Rate
100
69.80
69.54
70.49
Time Allocation (%)
50
30.70
29.99
2.09
2.20
1.60
1.41
0-
0.00
0.00
0.00
31.32
31.32
31.32
29.96
29.96
29.96
-50
68.68
70.04
70.04
70.04
-100
Llama-3.1-8B (Cost $50)
Llama-3.1-8B (Cost $200)
Llema-3.1-70B (Cost $50)
Llama-3.1-708 (Cost $100)
(Cost $100)
(Cost $200)
Figure C7. Effects of Action Cost Time Allocation. This figure visualizes how different action settings of agent's proactive assistance seeking affect the agent's ' time allocation.
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
recovery progress ($C.2-C.44. While high-performing agents typically allocate 75% 95% of their time codebase exploration and context understanding through Env interaction and proactive assistance seeking (Fig. 7), successful recoveries prioritize early-stage codebase exploration (Fig. C2) that helps establish essential contextual understanding before attempting to propose solution. However, existing LLM agents' limited temporal and financial resource awareness (Fig. C3, Fig. C6, Fig. C7) leave substantial room for future improvements strategic action planning and resource-efficeen collaborations.
These findings reveal that current LLM agents lack meaningful resouree awareness ($4.7), with their performance primarily determined by underlying capabilities without strategic resource management. These limitations present significant opportunities for enhancing agents' resource-aware planning and decision-making in collaborative systems.
C.6. Recovery Efficiency
Based efficiency evaluation metric (Eq. 5), evaluate the recovery efficiency of different LLM agents in Tab. C8 (under the standard experiment setting: 30-turn maximum, with initial budget of $1000, along with the same $100 action cost for both solution proposal and assistance seeking), where (I) time efficiency, denoted ftime is calculated as the average percentage of time taken in all out-of-sync tasks to the total 30-turn available time, and (2) expense efficiency, calculated as Jespense (%). is the percentage of the average financial expenditure among all recovery tasks. As spending less time and costs leads to high-efficiency out-of-sync recoveries, the actual recovery efficiency at both temporal and financial dimensions are inversely proportional the calculated E ftme and E eexensee scores, respectively.
EJJexpeneee
Our analysis of agents' out-of-sync recovery efficiency also supports previous observations and discussions ($4.7 & SC.5), providing insights for future development of resource-efficient collaborative systems.
Table C8. Out-af-Syne Recovery Efficiency.
This table summarizes agents' recovery efficiency in their standard 30-tum out-of-sync tasks. Following Tab. Cl, Aculiahonato represents the influence of collaborator assistance.
Agent
Independent (%)
Collaborative (%)
coulabbrator
(%
ftime
Ef eexpense
SR
Ef ftime
E.f expencee
SR
E.f ftime
Ef Effeprence
SR
Llama-3.1-8B
99.38
93.97
0.33
99,43
93.53
1.33
+0.05
-0.44
+1.00
Llama-3.1-70B
98.36
89.87
2.67
97.92
91.37
3.33
-0.44
+1.50
+0.66
GPT-4o mini
97.98
3.57
3.99
97.49
8.73
5.32
-0.49
+5.16
+1.33
DeepSeek
94.81
25.23
7.33
95.03
30.30
7.67
+0.22
+5.07
+0.34
GPT-4o
97.62
67.60
4.00
95.79
67.40
8.00
-1.83
-0.20
+4.00
Llama-3.3-70B
90.32
45.23
16.33
88.33
47.87
19.00
-1.99
+2.64
+2.67
Claude-3.5-Sonne
82.73
51.47
28.18
81.79
59.47
33.70
-0.94
+8.00
+5.52
Repo- Wise Performance
Independent - LAcu
LAye
Independent
Collabo ative 1A4u
Collabaratie
Mean
30
14
15
17
18
20
21
Figure C8. Repo- Wise Performance. Visualization of LLM agents" average performance at the repository level.
C.7. Repo- Pese Wise Analysis
We visualize repository-wind wise average performance on all LLM agents in Fig. C8, which illustrates consistent variation trends among evaluation metrics, suggesting an inverse correlation between an agent's recovery performance and out-of-syne task complexity (64.6).
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
D. Interaction Examples
D.I. Instructions on Out-of-Syne Recovery
In both independent and collaborative recoveries, we instruct agents to complete the task by acknowledging the primary task objectives and interactive constraints. For independent agents, we emphasize prompts the two available recovery actions: Env exploration and solution proposal. As to collaborative agents, the additional action of prouctively seeking collaborator assistance is further provided support their recovery completion.
Instruction Base.
The basic instruction prompt illuminates the primary task objectives and interactive constraints.
SYSTEM:
You are a helpful assistant.
**Task:** You are generating Python code for the Python repository "test_reee at v to fix the initial execution error of by the USER. Propose your solution to USER througa message when you are ready, and the USER will evaluate both your textual solution answer and your revised "test_repo" to give you feedback. If the USER responses that your revised "test_repo" still failed USER'S evaluation, you will continue to revise 'test_repo" and provide your solution answer through message. **Notice:*
given
Your task is to revise "test_reph to fix the initial execution error, and you MUST complete this task on your own without asking for human help. To generate the correct code, you can ONLY interact with the interactive Python (Cupyter Notebook) environment using "<execute "ppehhonnpp tag, and any other tools cannot be used.
Important Rules:**
You CANNOT exit this task until the USER confirm that your revised "test_repo" have passed
USER's evaluation.
You CANNOT evaluate your revised 'test_repo" on your own and state that "test_repo" passes USER's s evaluation and exit this task. Evaluation of your revised 'test_repo" MUST be conducted the USER after you choose "Option (b) " and provide your answer to the USER through message.
Please noted that is very unwise to run all unit tests on your side even just for testing or ckecking because other code files in 'test_repo" that are irrelevant to the
error log provided by the USER may currently be under USER's revision and therefore
cause unit test errors. However, your task is to fix ONLY the error given by the USER.
The Python virtual environment for this task has already been set up for you and you car. find the virtual environment at //workspace/test__eee tttepaceeteet__ee///////eaeeee Noted that the Python environment is well-prepared with all necessary dependencies installed, and therefore you CANNOT install any additional Python packages to assist your code revision.
weeeee/eettttttt'..eee To use this virtual
envi ronment, run "source
ONLY when the user confirmed that your revised Python repository "test_repo" has successfully passed USER's evaluation can you end this task and run the following command to exit: <execute_bash> exit </axecute_bash>.
Independent Recovery. Recovery instruction prompt for independent agents explains in detail the foundational recovery action choices and functioning constraints as follows:
At each turn, you have two options (a) and (b):
(a) Interact with the Python programming envi ronment and receive corresponding output to assist your code revision.
(b) Propose your solution, including (1) directly revising the responsible Python code
of "test_repo inside this Python repository at (self. weelf.container workspacel, I and
(2) providing your textual soluticn answer that incorporates both the absoluate path of your revised Python file and the nane of your revised function/method by
sending your answer to USER through message that adheres to the required format.
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
you choose "Option (a) Interaction with the Python programming envi onviretee""", you should
provide your textual explanation and analysis of your interaction through message,
including your textual explanation of both your execution command and the environment
output, which should be enclosed using "<env>" tag, for example: <env> I used the command "Is" to locate the respensible Python code. </env>.
On the other hand, if you choose "Option (b). Provide your solution", you should:
(1) Revise the responsible Python code of "test_repo" with proper indentation, which should be directly implemented inside the Python repository at
(2) Provide the absolute path of your revised Pytnon file and the name your revised
functicn/method as your solution by sending your sclution answer to USER through
message, whi ch MUST contain ONLY one 1ine of the absolute path followen by another
line ef the cunction/methen nare without any other texts and be enclosed using "<text>" tag, for example:
<text>
/workspace/test /nnrl//aereeettrpppsss/////iiii repp/err//mmmiffeerrr..... inference_prepare
</text>
If you revised method code, MUST provide ONLY the name your revised method and MUST NOT provide the name of the Python class containing your revised method (Ntextit(e.g.,)
is the name of your revised method, but NOT the Python class). If you medified more than one files or funetions/mechole MUST write line of the absolute Python file path followed by one function/method name for each two lines of your answer, for example: <text> hhorr//ppoorsssstreeee///////onr in inferenceepreparee
inference
</text>
Either you choose to ((a) Interact with the Python environment" or (b) Propose your solution', you MUST send message to the USER to evaluate your solution and provide feedback.
the beginning, you have an initial budget $1000. Refore correctly propose solution, each of your (b) Preposing a solution' attempts costs $100. Meanwhile, although you may make as many (a) Python environment interaction' turns as you want at no cost, you have in total 30 turns to complete this task. You will fail this task if you use up all your $1000 balance reach the maximum 30-turn 1imit without generating correct (b) Proposing solution' response. Therefore, please arrange each of your actions wisely.
Collaborative Recovery. Recovery instruction prompt for collaborative agents includes recovery choices for both independent actions and collaborative interactions.
At each turn, you have three opticns (a), (b), and (c)
(a) Interact with the Python programming environment and receive corresponding output to assist your code revision.
(b) Propose your solution, including (1) directly revising the responsible Python code of "test_repo" inside this Python repository at contaaccot.ieer. ingonaaine__oooeppceee) and
(2) providing your textual solution answer that incorporates both the absoluate path of your revised Pythen file and the name of your revised function/method by sending your answer to USER through message that adheres to the zequired format.
(c) Ask human question and receive the corresponding answer to assist your code revision.
If you choose "Option (a) Interacticn with the Python programming rnvinanme""", you should provide your textual explanation and analysis of your interaction through message, including your textual explanation of both your execution command and the environment output, which should be enclosed using "cenv>" tag, for example: <env> I used the command "Is" locate the respensible Python code. </env>
you chocse "Option (b) Propose your solution", you should:
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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
(1) Revise the responsible Pythen code of "test_repo" with proper indentation, which should be directly implemented inside the Python repository at
Provide the absolute path of your revised Python file and the name of your revised USER through
functicn/method as your solution by sending your solution
message, which MUST contain CNLY one line of the absolute path followed by another
line of the function/methor name without any other texts and be enclosed using "<text>" tag, for example:
<text>
inference_preparee
</text>
If you revised method code, MUST provide ONLY the name of your revised method and of the Python class containing your revised method
MUST NOT provide the "ineerence_prepare" (\textitle.g..l "inference prepare" is the name of your revised method, but NOT the Python class) If you modified more than one files or sunctions/methowe MDST write one 1ine of the absolute Fython file path followed by one function/meethd name for each two lines of your answer, for example: <text> wwecspppe//sssrrrree//////niiiis
inference_ inference_preparee
prepare
inference_util
</text>
If you choose "Option (c) Ask for huran assistance", you should provide your question through message, which should be enclosed using "<question>" tag and started with
"[UUSSTOO]], for exampie: <question> [QUESTION] what programming languages are used "test_repc"? </question>,
No matter which option you choose among (s) (b) and (c), you MUST send message to the USER to evaluate your response and provide feedback.
In the beginning, you have an initial budget of $1000. Before correctly propose a
soluticn, each of your (b) Proposing a solution' attempts costs $10C, while each of your (c) Asking for human's assistance" attempts costs $100. Meanwhile, although you may make as many (a) Python renrent interaction" atterpts you want at cost, you have in total attempts to cemplete this task. You will fail this task if you use up all your $1000 budget or reach the maximum 30-attempt limit without generating correct (bb) Proposing soluticn" response. Therefore, please arrange each of your actions wisely.
.*Tips** Try (c) Ask for human assistance" at any turns! This can definitely help accelerate your progress of proposing a correct solution and complete your task!
D.2. Resource Awareness
To notify agents their resouree consumption and conditions, incorporate resource awareness into task instructions and collaborator responses to help establish agents' awareness of resources.
For task instruction, we emphasize at the very end the resource constrains for current out-of-sync recovery task:
For independent agents:
the beginning, you have an initial budget $1000. Before correctly propose solution, each of your (b) Proposing a solution' attempts costs $10C. Meanwhile, although you may make as many (a) Python environment interaction" turns as you want at no cost, you have in total 30 turns to complete this task. You will fail this task if you use up all your $1000 balance reach the maximum 30-turn. 1imit without generating correct (b) Proposing solution" response. Therefore, please arrange each of your actions wisely.
For collaborative agents:
35

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SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
the beginning, you have an initial budget of $1000. Before correctly propose
solution, each of your ((b) Propcsing a solution" attempta costs $10c, while each of your " (c) Asking for human's 's assistance" attempts costs $100. Meanwhile, although you may make as many ((a) Python envircnment interaction" attempts as you want at no cost, you have in total 30 attempts to complete this task. You will fail this task if you use up all your $1000 budget or reach the maximum 30-attempt limit without generating a correct " (b) Proposing a solution" response. Therefore, please arrange each of your actions wisely.
In collaborators' responses, the remaining balance of current task is specifically displayed at the very beginning to remind agents of their resource consumption. For example, after two solution proposal attempts without success, an independent agent would receive:
[Balance: $800 L
Your revised "test_ repo" still failed USER'S evaluation test.
D.3. USER Prompt
User inputs provide agents with important task-specific information, including initial budget and codebase execution error. The relevant locations and names of potential responsible functions are also implied in the provided error log, while the exact out-of-sync function of the current task requires the agent's exploration to effectively identify the root causes and accurately localize its relative path.
Here is user prompt example for the out-of-syne recovery task on Callee psf requests dataset:
[Budget:
$10001 Your revised "test repo" failed execution test as follows:
short test 
FAILED FAILED ort.aaaaeesaaaaeiiaaaaaaaaaaaaaa
(HTTP:/ ortpapaaeeaaaaaaaaaaoaaaaaaaaaaa
FAILED FAILED ottp://weeectataaaaaooaaaaaaaaaa
FAILED http:///g.eeeaassscc..coorsorsss
FAILED http:://esse.ttttt..e.rrrrraaasr http:////ee/aaaarra..rro.arrrraa
FAILED
FAILED http:///eeeeetttrr.....//ooaaae2
4 [rtpepapeeeeaee..........aaaaaar
FAILED
RRCTS
FAILED http:///wa..aattc......ccsaaaaca
PAILED http:///|eeeaaaaae........aaaaar
FAILED oatp://g....aaataa...ttrrrrraraa
FAILED http::/ss....eet........ooo..ser
FAILED htgp:///w.aa.aaaa....aaaaaaaaarr
FAILED oea.:h/cceccccaaaaaaa...aaaaaaae PAILED org.ahag.eeaaaaiiaaa.aaaaaaaaaar
FAILED orgpah/wwehhaaaaa.aaaaaaaaaaaaar
FAILED orgpahac....aaaaa.aaaa.aaaaaaaar
FAILED ottp://see....nnn.....o.onoaarar
FAILED ortp://ceeeaataaaaaaaaaaaaaaaaar
FAILED tttp:///ee.gcniian...aaaaataaaaa
FAILED
FAILED ottp:///eeeeenssa.....o..c.oaooc
FAILED http://sseeeeaaaa......aaaaaaaae
FAILED orgps///eee.aaaaaa....oaaoaaaoaa
PAILED heap::/c.eettttrrrrerrrerrraraer
FAILED http://wwssaannaa..aaoaaaaaaaaar
FAILED http:///.eeeettt......e..eaaaaaa
FAILED ottp://w....tttaaaa.ooo.ooaaaaar
FAILED ottp:///eeeaaaaai..eaccaocaaaaaa
FAILED ort.ahaceseeaaarrr.ooo.a.aaaaaar
FAILED FAILED ottpp///ww.ccaaaaiooooorrraaaaar
FAILED org.ahaeeeeaaaaaa.a...aaaaaaaaar
PAILED
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SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
FAILED
FAILED http://see.aaaa.e....eeeeeeeeerr
"ypeBrro... -
20t.aaa.__eaa.........0000.0.000 FAILED otg.::/ccoaaaaiiaaooooooooooooaa onnecticnError]
-
FAILED
FAILED FAILED FAILED FAILED request 
TypeError: _....
.....
PATLRD red_request FAILED FAILED
prg.aaaaeaaaaaaaaaaaaaaaaaaaaaar with redirects FAILED ottp://wwwaaaaaaaa.ooo.o......aa
FAILED
request
FAILED http://wweecaaiioooaocaaaaaaaaar
Type...
FAILED
Ty... FAILED ottp://ae.eeeatar......a.aaaaaar
FAILED orgp://......eeaa.i.....raarraar
_OOKIES TypeErron:
FAILED ottp:://we.eaaaaar.....aaaaaaaaa ortp://eee.ciiiiioo...aaaaaaaaaa
FAILED
MERO HTTF_ _0___11___ GET
FAILED orgp://ceeeaaaaaaaaa...araaraard
VALUE
FAILED orgp://w....aaaaa.......aaaaaaaa
Iy...
PAILED hrgpa//ww...paaaaaa....aaoaaaarr
Typee... -
FAILED orgp://ww....aaaa.......aaaaaaaa
TER
PAILED orgeepaeee...aai........aaaaeaea
_AATAAAAAAAAA
FAILED ottp:://w...attt........oaaaaaar
FAILED ottp:///.....aaar......rpparaaar TypeErroz:...
FAILED http://see...eeat......iroioager
TypeErron:...
FAILED octp://cceeeettt....rr.aaaaaaaae TypeError... -x
FAILED ocgp://peeeatttaariaoo.o.aaaaaaa
FAILED orgpa/ac.eeaaaaaaaaaoaoaaaaaaaar FAILED http:///weecaaaaaa.acassaaaaaaar
PAILED ottp://eee..ett.......ooo.oaaaae
FAILED orwp://seeecaaaaa.......oaaaaaar
FAILED ottp:://eeeccaaaaaaaaaaoaaaaaaar FAILED http:///w....aaaaaa......a.aaaar FAILED http:///....aana.a......a.aaaaar FAILED ottp://eeeeeaaaiiiioooooaieneaea FAILED orapgh/ceeeeatacr.aaaaaaaaareeee FAILED orapgh/...eeeaarrioooooooocoeeer FAILED http:///w..eetttttaaaaaaaaaaaaaa
-ypeE...........................
Typ...
TypeE...........................
param
FAILED hatp:///eeeaaaaaa..a..aaaaaaaaaa
FAILED org.apaeeeeattaaaaaaaaaaaaaaaaar
FAILED orgp://eeeeeaaaaanatoaoooaaaaaae
FAILED ort.ap/p...aaaaaa.........aaaaaa FAILED oetp:///...eetttt....ooooaaaaaaa PAILED orwpa//.....aaaaa......aaaaaarar PAILED ottp://eeeeetttaa.aaooooooaaaaaa
FAILED
Ty.er...........................
PAILED http://www...a...a..........ooar
FAILED hrg.a//iih...aaaa........irrrrrr -yp...
FAILED org.aa/eeeeeaaai.aaiiaaiiarrranr Typ...
FAILED orgpahagh..ctttaaaaaao.ooaoaaaar FAILED hrapg//..e.ecccaa...ooooooooooed
host
FAILED org.ehaw...sscai..........c.aaaa
FAILED
FAILED orgpahahehhattiiiiiiiiaaaaaaaaaa
FAILED
FAILED orgpa///heeaaaiaiaai......arr.a.
FAILED
FAILED Brgeaeaeeeeecaiaiaiaaaaaaaaeanne
FAILED
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SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
FAILED
FAILED
-ypperrr........................
FAILED ottp://ww.eaaaaooaoooooooaaoaaar
FAILED orwpa//ww....aaaaa......aaaaaaar
FAILED
FAILED
FAILED ottp:///weeeaaaaaaaaao...a..aaar
FAILED ottp:///weeaaaaaaaiii.aaaaaaaaar
FAILED orgpahac...etttraaaaaaaaaaaaaaar FAILED ort.ah/g....tttaaaaaaaaaaaaaaaar PATIED org.apaaee..aaiiaiiaarrrarrrrrrr otwpa//wwee.aaaaaa.a...oaaaaaaar
-yee. TypeE...
PATLED ection httpa//wwwaaaaaaaaaaaaaaaaaaaaaa
FAILED FAILED FAILED prt.apaieeeeeeetta.rrr.caaiiiair TypeErzoz: :
content is None
mixin Typ...
FAILED http:///|eeeaeaooroc/.////ooooor FAILED httpa//ceee.eeee.aa...........a. mfoommust be an int, float
htt.aaaaao_aaaattttttttttttttttt (on.r/oooooottooooootoooonntteot (connect, read)
or Nonc] httpp//eeeeeeeetrrrr../.r//eaaar
FAILED FAILED FAILED htap:///e.e.aaaaaaa.cc.c.....a.r FAILED ort.a//eeeaaaaiiaaaa.a.rr.raarrr Typ... FAILED org.aha.....aataaaaaaaa.....aaar
TypeErr...
Typ...
Typ...
FAILED ort.aha.....aataaaaaa......aaaar meouutt]
PAILED http://|eeeppaaaao.aoooaooooeeor
FAILED http:///w..aaaaaaa.......ooaoaaa
FAILED Xrgprhag....aataaaa....aaaaaaaar FAILED http:///eeeeetttarra....//ooooor FAILED ottpa//hweheaaaaaa.......aaaaaar nvalidURL] FAILED org.ahacee.aaaaaaaao.r..aaaaaaaa FAILED ottp:///...oott..eaaea.aoooooooe FAILED htt.a:/wwhhtttttraeaaa.aa.aaaaaa wwt.ugg...asssnnnneddee.a.aeee..
FAILED _ - FAILED httpaaaawacatttii......aaaaaaaaa http:///wwwaaaaaa.......ooo.onrr
htgplggwweeettyyuudddeeeeeeeeeee cert
FAILED settings
131 failed, 196 passed, skipped, xfailed, warnings in 30.10s
D.4. Reasoning for Out-of-Sync Recovery
Agents' successful recoveries from their out-of-sync states depend heavily on their reasoning effectiveness, particularly how efficiently they gather information and how well they understand and utilize By examining both their thought patterns and solution implementations throughout their out-of-sync recovery journeys, we analyze their reasoning process detail. is evident in two key areas: (1) their exploratory thinking while interacting with the Env collaborators, showing how they plan interactions and analyze problems; and (2) their reflective thinking about failed solutions, demonstrating their progressive reasoning toward recovery success.
The following example showcases an agent's thoughts on out-of-sync causes, reasoning through seeking collaborator assistance and developing recovery solutions, while its misinterpretation collaborator responses (e.g., collaborator suggests no missing imports) leads its ultimate recovery failure.
TURN 17
AGENT THOUGHTS:
38

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
I've found the "test_applycli_doob"
test starting at 1ine 797. Let me check the test implementation and the imports at the top of the file. The error message indicates that "get_data_path" is missing from the imports. This function is likely needed for the test cases.
Let ask question get information:
TURN 18
AGENT THOUGHTS:
Thank you for the information. New fix the missing import. I'll add "get_data_
to the imports from ppacc.uuill
TURN 19
Now that I I've added the missing import, I should propose my solution of nce ''ve fixed the import error:
<text>
/workspace/test. hor.t/aaccessss.eeee/eee//ee/e/s tepp/ssacy/teettstee...... cli.py test_ dest_applycli_docbii
</text>
The following reasoning thoughts are extracted from a successful out-of-sync recovery, which illuminates coherent reasoning process through combining high-quality queries and Env exploration to gather information of significance and reasoning about potential solutions towards out-of-sync recovery success:
TURN 1
AGENT THOUGHTS;
Let me start by examining the test file 's showing errors.
TURN
AGENT THOUGHTS:
Let me scroll down to find the relevant test case that's failing.
asssessess TURN 3 ==ssesssss
TURN 10
AGENT THOUGHTS:
let me continun scrolling to find the failing test case about cert fatt bundles.
. TURN 11
AGENT THOUGHTS:
found the failing test case "test env env_cert bundles'. Let me ask question better understand the issue.
TURN 12
AGENT THOUGHTS:
understand the issuc now, The test is failing because it's trying to use a pytest fixture "mocker" but it's not properly set up. Let me check the test file to see where this fixture should be defined.
TURN 13
AGENT THOUGHTS:
I see the issue. The test is using pytest's "mocker" tixture but it not installed. Let me check if pytest-mock is installed.
TURN 14
AGENT THOUGHTS:
I see that pytest-mock is not installed. Let me modify the test file to use different approach without relying the nocker fixture.
TURN 15
AGENT THOUGHTS:
Let me fix the syntax error in my edit.
TURN 16
AGENT THOUGHTS:
39

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
I've modified the test to use direct environment variable manipulation instead mocking, 4 Now let me propose this as the solution:
<text>
test_ env cev_cert_bbote bundles </text>
D.5. LLMs as Know-Everything Collaborators
Leveraging LLMs as know-everything collaborators ($3.3) to assist agents' out-of-sync recoveries, we provide them with complete task-specific knowledge to establish the know-eveyything answerer for each out-of-sync task. As shown in the following prompting example, not only do we incorporate all necessary contextual information and ground-truth solution into the prompts, but also define concrete restrictions their response formation.
SYSTEM:
You are a helpful assistant. You are helping the USER to provide question anawering assistance to USER's 's students.
*Grading Context:*.
In the Pythen repository "test_repo" locating //ooksspac/test_repo there is out-of-sync function aissert_format (denoted "original_code"" in the Python file "util.py" locating at This out-oo--yyot "original_code of the function assert_format" is as follows: "original_code"=
def assert f
source: str, expected: str, mode: black. Mode DEFAULT_MODE,
",
fast: bool False,
minimum_veesee version: Optional [Tuple int]] = None,
None:
""*Convenience function to check that Black formats expected.
You can pass mminimun_version if you're re passing code with newer syntax to guard safety quards so they don't just crash with Syntaxırror, Please note this is separate from Targetverson Mode configuration.
***
actual Alack..orrat str (source, mode=mode)
fnverr__formttt equull__uulll (expected, actual)
It's net useful to run safety checks if we're expecting no changes anyway. The assertion right above will raise if reality does actually make changes. This just avoids wasted CPU cycles.
if not fast and source != expected:
Unfortunately the AST equivalence check relies on the built-in ast module being able to parse the code being formatted. This doesn't always work out when checking modern code clder versions.
if minimum_versiin is None or vessversion info minimun abscktassert_equiiveee assck. stable (source, actual, mode=mode)
(source, actual)
This "original_ccde" out-of-sync because the Python repository "test_repo" has been
+ updated except the function 'assert_formatt still remains as the old-version poriginal_code" Therefore, running unit test on the updated "test "test_repo" repo" that contains this out-of-syne "original_code reports the following error (denoted as "initial_executionnn : "initial ettpiialleeecttioneereeee eerorr==
test session starts cemeeesoee
linux - Python 3.11.9, pytest-8.3.2 3.2, pluggy-1.5.0 -- workkgpec//tttt..ee///////yyyyo
cachedir: -pytest_acche
hypothesis prefile 'default' database @ttabaaeeleeeeyyyaaaaaaaaaaaaaae (Posi
ace/test_repo/ hteetess_rrep//hhphhheessss /
rootdir: /workspace/test_reee
40

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
configfile: pyproject.
plugins: hypothesis-6.122 0, asyncic-0.24. 24.0
asyncio: mode-Mode, STRRIIC STRICT, de @efault_loop_scoeeeett
collecting collected 176 items
py39] FAILED
0&]
www.rss_wwwaaaaaaaaaaaaaeeeees FAILED
1$]
FAILED 1$]
FAILED
2%]
PATLED
28]
orgaaaaeeeecaaaaaooaaaaannnannar PAT LRD
34]
3&1
FAILED
4%
FAILED
FAILED
58]
FAILED
6$]
FAILED
[ 6%]
FAILED
7%]
FAILED
78]
FAILED [ 88]
98]
9%
FAILED [ 10%]
FAILED PAILED
10%]
[1&]
11&]
3101
FAILED 128]
138]
newlinee]
FAILED [ 138]
FAILED
148]
FAILED [ 14%]
FAILED
15%]
FAILED
[ 158]
FAI100
166]
FAILED FAILED
177]]
FAILED
178]
FAILED
188]
3ra.e expression
tests/test_
PAILED 18%]
FAILED 198]
FAILED 196]
hrtpa///eeetiteaaaaaa..aa..aaaia FAILED
FAILED hr
TypeError: assert...
FAILED orgpaaa.....aaaaaaaoaao.aooooaar
Type...
FAILED hrgp:///he.c.att............aaor
TypeE -yyp:/ooo..0py00o.....00
TypeBzro...
FAILED FAILED [brackets] hrt.://weee.ciaaaaa......nannnnn
FAILED
PAILED httpp///eeeectttoo......o..aaaar
FAILED
by class]
FAILED ora.apaiie.ccaaaaaaa.aaaaaaaaaar FAILED org.apaeeeeettiiii..aiinaannaaan
TypeÉrzor:
underscoreessss o
FAILED |rgpahaehhaaaaaaiaiooo.......aan
FAILED hrg.ah/gheaaannnaaaaaaaaaaaaaaar
TypeBroor: :s8.:::a_8......................
FAILED
FAILED http://eatt_connnoooooo.aeeeaee_ -
[basic] Typešrror...
174 failed, 2 passed in 1.47g

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
**w Questions for S Running unit test on the Python repository "test_repo" (here the updated "test_repo" that contains the out-of-syne "original_code" is provided to students) reports the following error (here "Initial_execution_eee
is of both the Python file path of the responsible function/methed code and the nane of the responsible function/method code, and (2) revise the responsible function/method code you just lecalized to fix "Inttial_cxccution.cee
provided to students). Students are asked to: (1) localize the responsible functicn/method code that caused this error, and provide your
**Ground-truth Answers for Two Questions**
(1) Python file path: Name of the responsible function. assert_format (2) "ground_truth_reviss coode"
def aesert_format source: str, expected: str,
mode: black.Mode DEFAULT_MODE.
fast: bool False,
minimum_version versiors: Optional [Tuple[ing) int]] = None, lines: Collection (upleeint, int]] = (),
no htrereeww_iee__eegtthh
bool False,
None:
"""Convenience function to check that Black formats as expected.
You pass eminimun_versien if you're passing code with newer syntax guard safety guards they don't just crash with SyntaxError. Flease this is configuration.
separate TargetVerson
assserr format (
source, expected, mode, fast=fast, miiimmn.yrrrsoommmiimmmnueeee.. lines=lincs
For beth preview and nen-preview tests, ensure that Black doesn't crash this code, but don't pass "expected" because the precise output may differ. try:
it mode. unstable:
new mode = replace (mode, unstable=False, preview=False)
else:
new_mode replace (mode, preview-not code.previen)
aasert_format_innee
source,
None,
new new_mode,
fast=fast,
lines=lines,
except Exception
text
"unstable" it mode. unstable
else "non-preview" if mode.previer else "preview"
raise FormatFailure
f"Black crashed formatting this case in (text) mode.
from
Similarly, setting line length is good way catch stability bugs. Some tests are known to be broken preview mode with line length
of 1 though, and have marked that with flag -no--pevvior-Lne---eee seeview_modee [False] if not no_preview line Line_ length
42

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SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
preview_modes. apppad append (True)
for preview_mode in preview_nodes:
try:
_assert_formaae format_inned
source,
None,
replace (mode, proriew-preview__ode
line_length=1, unstable=ralse), fast-fast, wmmmun_.eersscoomiiinmmm..oo lines-lines,
except Exception as e:
text = "preview" if preview_mode mode else "non-previex" raise FormatFailure
f"Black crashed formatting this case in (text) mode with
+ line-length-1."
) from
**Questoo Answering Assistance:sx To help students better answer these two questions,
each student is allowed to ask you a question. However, your answer to each student
question has following restrictions:
(1) You CAN ONLY answer the specific piece of information asked by the student, and CANNOT
include any other information NOT asked by the student.
(2) You CANNOT provide any misleading information if you are unsure its correctness.
T+ D0:** Please answer each student's question provided by the user.
Importan:: MUST give ONLY your answer your response. MUST NOT give any other things like rhetorical questiens, question analysis, enclosure synbols, etc.
E. Out-of-Syne Recovery Examples
E.1. Success Cases
(1) Independent Recovery Success
Here is an interaction example of a successful independent out-of-symc recovery completion on explosion/spuCy, performed
by Claude-3.5 3.5dee-335S00 S-Sonnet:
SYSTEM:
You are a helpful assistant. **Takk:* You are generating Pythen code for the Python repository "test_repo" at to fix the initial exccution error "test_repo" given by USER. Prepose your solution to USER through message when you are ready, and the USER will evaluate both your textual solution answer and your revised "test_repo" to give you feedback. If the USER responses that your revised 'test_repa still failed USER's evaluaticn, you will continue to revise "test_repo" and provide your solution answer through message.
//worksaac/teet__rppp
**Notice:**
Your task is to revise 'test_repo" to fix the initial execution error, and you MUST complete this task on your own without asking for human help. To generate the correct code, you can ONLY interact with the interactive Python (Jupyter Notebook) environment using "<execute "<execute__python>>> ipython>* tag, and any other tools cannot be used. At each turn, you have two options (a) and (b):
(a) Interact with the Python programming environment and receive corresponding output to assist your code revisicn.
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(b) Propose your solution, including (1) directly revising the responsible Python code and (2)
of "test_repo" inside this Python repository at /sorkapace/test_
providing your textual solution answer that incorporates both the absoluate path of your revised Python file and the name of your revised funetion/method by sending your answer to USER through message that adheres to the zequired format.
If you choose "Option (a) Interaction with the Python programming environment you should provide your textual explanation and analysis of your interaction through message, including your textual explanat ion of both your execution command and the environment
enclosed using "<env>" tag, for example: <env> used the command "1s" to locate the respensible Python code. </env>
output, which should
on the other hand, if you choose "Option (b) Provide your solution", you should:
(1) Revise the responsible Pythen code of "test_repo" with proper indentati on,, which should be directly implemented inside the Python repository at
//orkspace//estt teapce//tttttttprpp.eeee
(2) Provide the absolute path of your revised Python file and the name of your revised functicn/method as your solution by sending your solution answer to USER through message, which MUST contain ONLY one 1ine of the absolute path followed by another line of the function/methed name without any other texts and be enclosed using "<text>" tag, for example: <text> wwokkpacc/testrrep////see wttsss///uninierenne..... .yy
inference_ </text>. If you revised a method code, MUST provide ONLY the name of your revised method and MUST NOT provide the nane of the Python class containing your revised method (ltextit(e.g.r but NOT the Python class) If you medified MUST write one line of the absclute Python tile path followed by one "unction/nethod name for each two lines of your answer, for example: <text> inference_prepare
"inference "inference_prepare" preepare"r is the name of your revised method,
than one files funetions/methoda
inference_ute </text>,
Either you choose to ((a) Interact with the Python environment" or (b) Propose your soluticn', you MUST send message to the USER to evaluate your solution and provide feedback.
+mporrtan Rules**
You CANNOT exit this task until the USER confirm that your revised "test_ropo" have passed USER's evaluation.
You CANNOT evaluate your revised 'test_repo" on your and state that
passes
USER's evaluation and exit this task. Rvaluation of your revised 'test_repo" MUST be conducted by the USER after you cheose "Option (b) and provide your answer to the USER through message.
Please noted that it is very unwise to run all unit tests on your side even just for testing or ckecking because ether code files in 'test_repo" that are irrelevant to the error log provided by the USER may currently
under USER's revision and therefore cause unit test errors. However, your task is to fix ONLY the error given by the USER. Python virtual environment for this task has already been set up for you and you can find To use this virtual environment,
the virtual environment at "/xerkspace/test_eeny/ run "source /wwrkspace/test_ //ock/geee/tsstsuee//////aaatttt wttp////////aaattatttt... Noted that the Python environment well-prepared with all necessary dependencies installed, and therefore you CANNCT install any additional Python packages to assist your code revision.
ONLY when the user confi rmed that your revised Python repository 'test_repo' has successfully passed USER'S evaluation can you end this task and run the foll following
command to exit: cexecute_aasie
exit </execute_bash>
** Important Neti In the beginning, you have an initial budget of S 00000 Hefore
correctly prepose solution, each of your (bb Proposing solution" attempts costs
$100. Meanwhile, although you may make as many ((a) Python environment interaction" attempts as you want no cost, you have in total 30 attempts to complete this task.
You will fail this task if you use up all your $1000 budget or reach the maximum
30-attempt limit without generating à correct ((b) Proposing a solution" response. Therefore, please arrange each of your actions wisely.
USER:
[Budget: $1000] Your revised 'test_repo' failed execution test as follows: [initial execution error]
ImportError while loading conftest //tptpppaeeeeettteeee//aaaaa//te initt py:1: <module>
44

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SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
from import pipeline 4 noqa:
specy_pppeeine//iiittt____ py:1: py:l: in <module>
from .ttrribaterullrr import AttributeRuler
in <module>
from ..aanguage import Language
spacy/langguag..yy::7:
<module>
DEFAULI_CONFIC util.,oad_confige out___..o...oaogggEEEEE..L.C.C.. (EFAATLOOOLTTLOOCGGGAAATTT
spacy/util.gy::718: in load_config
config_path ensure path
ensure_path (path)
spacy/uti1.py::399 ensure_pate
isinstance (path, basestring_)
K
Namehrror: name 'basestring_' is not defined
TURN 1
ASSISTANT:
[IFythonCode]
open_ (1ook//speeeeetrrrooo//////////p [Thought]
Let me analyze the error interact with the Python eavironment to locate the responsible code.
TURN 2
ASSISTANT:
[IPythonCode]
aoroll_downf
[Thought]
Let me scroll down to find the "ensure_path" function where the error occurs.
TURN
ASSISTANT:
[IPythonCode]
@eroll_downl
scroll_Gown ()
scroll_down()
[Thought]
Let me continue scrolling to find the "ensure_path" function.
TURN
ASSISTANT:
[IPythonCodel
edit_fileebbyrrelacce
/xorkspace/test_rpp/ wttppppppppppp..lllll.
to_replace=""" immort functools
import importlib
import importlib.util
import inspect
import itertocls
import logging
import os import pkgutil
import
import shlex
import shutil
import socket
import stat import subprocess
import sys
import tempfile
import warnings""",
tex_content="""mpprtt functools
import importlib
import importlib.util
import inspect
import itertools
import logging
45

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
import os
import pkgutil import re
import shlex
import shutil
import socket
import stat
import subprocess
import sys
import tempfile
import warnings
Define basestring_ for Python 3 corpatibility basestring_ str"""
[Thought]
found the issue. In the "ensure_path" function in
there's reference to "basestring_" which is not defined. This variable should be defined befere being used. Let me fix this by adding the definition "basestring_" at the beginning of the file.
TURN 5
ASSISTANT:
|MssaageAttinn
have fixed the issue by adding the definition of "basestring_" as a string type for Python compatibility. Here's my solution:
<text>
ensure_path path
</text>
The fix adds the "basestring_ str' the beginning of the file to define the missing "basestring_" variable that was causing the ImportError. This is a common
Python 2 to Python 3 compatibility fix, as "basestring" was removed in Python 3 and we now use "str' instead.
USER:
[Balance: $900 L Your revised "epot_repo" still failed USER's evaluation test. Please try again.
TURN
ASSISTANT:
[IPythonCode]
open_file file (' /ttp:///aaaeeeeesseeeppp//////cp [Thought]
Let me check the error more carefully. The issue might related to Python vs Python compatibility. Let me check the imports and compatibility section of the file.
TURN 7
ASSISTANT:
[ipythonCode]
adit_file_bee jeet_fill_b___eplacce
//xorkspace/test repo/ http:/saccy.llllc// to replace="""de ensure path (path): if isinstance (path, basestring return (path))th) else:
return path""",
rex_content="""de ensure_path (path): isinstance (path, (str, bytes)): return (path)
else:
return "ath"""
46

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
[Thought]
Let me check the "ensure_path" function again and modify it to use a more modern approach without relying on "basestring_'. Since we re working with Python 3, we should use 'str' and "Path" types directly.
TURN
ASSISTANT:
[IPythonCode]
eett_tiile_b__repleeee
"rreplacee"""mmpox" functools
import importlib
import inportlib.uti1
import inspect
import itertools
import logging
import os import pkgutil
import re import shlex
import shutil
import socket import stat
import subprocess
import sys
import tempfile
import warnings
Define basestring_ for Python 3 corpatibility basestring str"""
ree_content="""mport functocls import importlib
import importlib.util
import inspect
import itertccls
import logging
import os import pkgutil
import re import shlex
import shutil
import socket
import stat
import subprocess
import sys
import tempfile
import warnings""u
[Thought]
Let me remove the previously added "basestring_" definition since we're now using different approach with (str, bytes) in "ensure_path':
TURN 9
ASSISTANT:
[IPythonCode]
search filecchhislllbesstteeeii ('basestring_', [Thought]
Let me check if there are any other references to 'basestring_" in the file that need to be updated:
TURN 10
[IPythonCode]
search_dirr ('basestring_', //ookspacc/tet__rppp))
[Thought]
47

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Let me check if there are any other Python files or using "basestring_":
repository that might be importing
TURN 11
ASSISTANT:
[MessageActicn]
Let me check the full error message again to make sure we' re addressing the right issuc. Since we we've modified the "ensure_path" function to handle both "str" and "bytes" types, and there
other references to "basestring_" in the codebase, let propose my solution:
<text>
/workspace/ tiikspace/test_rppppeee http:///sssteere/e/p///ttttttttt
ensure path
</text>
have modified the "ensure_path" functicn to use Python 3's type checking with "isinstance (path, (str, bytes))" instead of the Python "basestring_". This change maintains compatibility with beth string and bytes objects while using modern Pythor type checking. The function now properly handles path-like objects, strings, and bytes objects.
USER:
[Balance:
$800 L Well done! Your revised "test_repo" has successfully passed USER'S
evaluation test!
(2) Collaborative Recovery Success
Here is an interaction example of successful collaborative out-of-sync recovery completion on scrapy/erapy, performed
by GPT-4o:
SYSTEM:
You are a helpful assistant.
**Takk:* You are generating Pythen code for the Python repository 'test_repo" at //ookkspcc/test__nee
to fix the initial execution error of 'test_repo" given by the USER. Prepose your solution to USER through message when you are ready, and the USER will evaluate both your textual solution answer and your revised "test_repo" you feedback. the USER respenses that your revised "test_reph still failed USER's evaluation, you will continue to revise "test_repo" and provide your solution answer through message.
give
NNtiice:**
Your task to revise 'test_repo' fix the initial execution error, and you may ask for human help. To generate the correct code, you can ONLY interact with the interactive Python (Jupyter Notebook) envi envirenmen renment using "<execute_ipython>> tag, and any other tools cannct be used.
At each turn, you have two options (a) and (b):
(a) Interact with the Python programming environment and receive corresponding output to assist your code revisicn.
(b) Propose your solution, including (1) directly revising the responsible Python code and (2)
of "test_repo" inside this Pythen repository at /workapace/test providing your textual solution answer
incorporates both the absoluate path of your revised Python file and the name of your revised funation/methhdd by sending your answer to USER through message that adheres to the required format. (9) Ask human question and receive the corresponding answer to assist your code revision.
If you choose "Option (a) Interacticn with the Python programming environmen=", you should provide your textual explanation and analysis of your interaction through message, including your textual explanation of both your execution command and the ronment output, which should be enclosed using "<env>" tag, for example: <env> used the command "Is" to locate the responsible Python code. </env>
If you choose "Option (b) Propose your solution", you should:
(1) Revise the responsible Python code of "test_repp with proper indentation, which
should be directly implemented inside the Python repository at //xorkspace/tett_rqpo
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Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
(2) Provide the absolute path your revised Python file and the name your revised functicn/method as your sclution by sending your solution answer to USER through message, which MUST contain ONLY one 1ine of the absolute path followed by another line of the hunction/methed nare without any other texts and be enclosed using "<text>" tag, for example: <text> inference_prepart </text>. If you revised a method code, MUST provide ONLY the name of your revised method and MUST NOI provide the name of the Python class containing your "inference, Linforence_preparce than files functions/methon MUST write ene line of the absclute Python file path followed by one function/nethod name for each two lines of your answer, for example: <text>
httpe//ttssssrpppp/rrrreeeee htap://rc/runiineerrnneee. inferencee.p.
revised methed (Atextit(e.g.) but NOT the Python class) If you modified
is the name of your revised method,
inference prepare
preeeee
wwrkssace/ttst._reeee wttpeessteeooo/reeeeeeeeee infereeneeee inference_util util
</text>.
If you choose "Option (c) Ask for huran assistance", you should provide your question through message, which should be enclosed using "<question>" tag and started with " "[QUESTION]" ", for example: <question> [QUESTION] Which function or method is responsible for the initial execution error, and what is its absolute path?
</questicn>.
No matter which option you choose among (a) (b) and (c), you MUST send message to the USER to evaluate your response and provide teedback.
Important Rules+*
You CANNOT exit this task until the USER confirm rm that your revised "test_repo" have passed
USER's evaluation.
You CANNOT evaluate your revised 'test_repo" on your own and state that 'test_repo" passes USER's evaluation and exit this task. Evaluation of your revised "test_reps MUST be conducted by the USER after you choose "Option (b) " and provide your answer to the USER through message.
Please noted that it is very unwise to all unit tests your side testing or ekecking because other code files "test_repo" that are irrelevant to the error log provided by the USER may currently be under USER's revision and therefore cause unit test errors. However, your task is to fix ONLY the error given by the USER. Python virtual environment for this task has already been set up for you and you can find the virtual rovmett at //xorkspace/test_vee"" run "source wwwckppaeeeeest..ee//b//aaaattte
just for
To this virtual envi ronment,
Noted that the Python environment is well-prepared with all necessary dependencies
installed, and therefore CANNOT install additionsl Python packages to assist your code revision.
ONLY when the user confirmed that your revised Python repository "test_repo" has puccessfully passed USER's evaluation can you end this task and run the following command to exit: <execute <execute_bash> exit //execute_bashn
** Important Notice** In the beginning, you have an initial budget of $1000. Before
correctly prepose solution, each of your ((b) Proposing solution" attempts costs $100, while each of your (() Asking for human assistance" attempts costs $100.
Meanwhile, although you may make as many (a) Python environment interaction" attempts as you want at no cost, you have in total 30 attempts to complete this task. You will
fail this task if you use up all your $1000 budget or reach the maximum 30-attempt limit without generating
correct (b) Proposing solution" response. Therefore, please arrange each of your actions wisely.
**Tips** Try (c) Ask for human assistance" at any turns! This can definitely help accelerate your progress of preposing correct solution and complete your task!
USER:
[Budget: $1000] Your revised "test_repo" failed execution test as follows: [initial execution error]
[...] (ponse
request, response, self.spider
/workspace/tent www.rreeem...eeeeett............ (request, self self.spider)
self, r
49

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SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
in request_acheduled org. request) .effrrrrr in referrer
wttppe/eee..ieereroo///////aaaaa and self set
self = 0x715fc8c7ab10>
object at
"Itte///scrayyeett.oo///3
def potentialll_.tuussoorhyy (sel,, r):
$ Note: this does not follow
parsed url (xc
NameErrer: name wurrpaase_cccttiiiie cached' is not defined
NameError
short test summary info
FAILED orw.aa/aec..auooooaoooororroaaar FAILED wrg.ahaseeeaaaaaaaaa.....aaaaaar
FAILED http:///we.ccccnaaaaaeeeeeeeseer FAILED http:///weaaaaaaaaaaacaa.aaaaaar ::test http:///ww..aannaaaa.eeer.etraar
FAILED tests/test..giidemlddddde..
PALLED ott.apaww....aaaaa.oo.......a.rr
sOrigin::test
failed, 33 passed in 0.35s
TURN 1
ASSISTANCE:
[Messagehcticn]
<question> [QUESTION] Could you please provide the absolute path of the file and the function or method name where the "urlparse_cached" function is defined or should be defined? </question>
USER:
[Balance: $900 Left The assolute path of the file where the "uriparse_ function should be defined is not provided in the given context, and therefore I am unsure of its correctness.
TURN
ASSISTANCE:
[IPythonCode]
search_dss dir ("urlparse_cschhe"" //nerkspace/eest_reo"
[Thought]
Let's start by searching for the "urlparse_cached" function in the "test_repo" directory to determine if it is defined clsewhere or if it needs to be defined. This will help us understand where the functicn should be located or if it needs to be implemented.
TURN
ASSI ITANCC:
[MessageAction <env>
The "urlparse cached" function is defined in the file
"cchhedd
at line 18. It also imported and used in various other files within the "test_repo" directory. The error in the initial execution leg indicates that the function is not recognized in the file which suggests that it
//wrrkspac//test. httpr/ppce/est.rrep/srrs//////ts
tworkspace/test.co http://swapppppiimmm.d.dl.aaaaa might not have been imported there.
</env>
I will now check the file
to
see if the "uriparse_caca"" eached" function is imported. If it is not, I will add the necessary import statement. Let 's open the file inspect the inports.
50

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
USER: [Balance: $800 Left] Your revised "test_repo" still failed USER'S evaluation test. Please try again.
TURN
ASSISTANCE:
[IPythonCode]
open_file file o
100)
[Thought]
let s open the file //war//pece//eeeeerrrrrrrrr////s rererreerrreeeeeeeeeeeeeerrrrrrr and inspect the imports to see if "urlparse_cached" is imported. If it is not, will add the
necessary import statement.
TURN
ASSISTANCE:
[MessageAction] <env>
The "urlparse_cachi" function not imported file httpp//raaaysppiddddiidii.
This is likely the cause of the "NameError' encountered during the test execution. To resolve this, I will add an import statement for "acchrs from "scrapy.utie utils. wtw.s.ttt...b.tb........ httpobj" </env>
will now add the import statement for turlparse_cacher in the file /ttppy//ppiie derm wwwp rel
[Balance: $700 Left] Your revised "test_repa still failed USER's evaluation test. Please try again.
TURN
ASSISTANCE:
[IFythonCode]
insert__ontert conatt__ooteet____inne
30,
'from wtt.ey.utll..htttoe. import ur.parse_cacnedva
[
Let 's add the import statement for 'urlparse_cached" in the file
cecesttrpp//crrp/eeeeeee http:///pppmmmmmmddeeeeeeeeeeeee www.mi/dwwwweeeeeeeeeer... will insert the import statement after the existing imports.
TURN 7 ===ssssss
ASSISTANCE:
[MessageAction]
<env>
have added the import statement for "uriparse_cachi" cached" in the file
wtteepace/ees_rrpp/ssreree dewe htweriddeeeee///eeeeerrrrrr referer.py". This should resolve the 'Naeerrror' encountered during the test execution.
</env>
I will new propese the solution to the user.
<text>
hettrpew..errrpppeeeee///cccccce potentialllytrrusworrtt
</text>
USER:
[Balance: $600 Left] Your revised 'test_repo" still failed USER's evaluation test. Please try again.
TURN
51

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
ASSISTANCE:
[1PythonCode]
Search_filed def potendiillly.trustterrb''''
[Thought]
Let 's further investigate the bnttentiallyy.trusttrtttt method in the file
to ensure that the jurlparse_cachede function is used cerrectly. I will search for the
"potentially method in the file to review its implementation.
TURN 9
ASSISTANCH
[IPythonCode]
open file http:///wwaceeeeee..///////////e
104, 100)
[Thought]
Let's open the file htt.s//p/s.s.teeooo///////////de
at line 104 method. This will help ensure that the 'urlparse_cached function is used correctly within this method.
to review the implementation of the
TURN 10
ASSISTANCE:
[MessageAction]
<env>
The potentially..tustooon
method uses the tur_parse_cachee function to parse the URL. The import statement for Ellparse_cachest has been added, so the "NameError" should be resolved. However, the methed also calls sellf.tt__rooecteee
which uses This could be inconsistent if "urlparse_cached" is intended to be used throughout. will update the 'tls_protected" method to use celparse_cachers for consistency.
"urlparse" instead of orrpaarse_acched
</env>
will update the "tle_protected" method to use "urlparse_cachea" instead of "urlparse". Let's proceed with this change.
USER:
[Balance: $500 Left] Your revised 'test_repo" still failed USER's evaluation test. Please try again.
TURN 11
ASSISTANCE:
[iPythonCode]
edit_filee_byrrelacee
to po_replace='retee return urlparse (url). scheme in ("https", "ftps") jou_content=treturr urlparse caaerse_ccchedduue (url) scheme in ("https", "ftps")'
[Thought]
Let's s update the "tis_protected" method to use "urlparse_cached" instead of "urlparse" for consistency. I will make this change in the file //wrkkspcce/esst_reee cecpsttrpppp/crrproeeeeeaa scrasy/rrpyyooee sarrapysppddemanet
TURN 12
ASSISTANCH,
[PPthhonCode]
edit file by replace(
to_replace='
return urlparse (url) scheme in ("https", "ftps")
nex_content='
return urlparse_cached (url) scchmee in ("https", "ftps")'
[Thought]
It seems there was an indentation error in the previous edit. Let's correct the indentation and update the "tls_protected" method to use 'urlparse_cached" instead of "urlparse".
TURN 13
52

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
ASSISTANCE:
[MessageAction]
<env>
have successfully updated the 'tls prs__etttettd" method to use "uriparse_cer cached" instead of "uriparse". This should consistency in the URL parsing approach used in the ppotentiall method and its related methods.
</env>
will propese the solution to the user.
<text>
htt.://////sssteeee////////////e tls_protected
</text>
USER:
[Balance: $400 Left] Your revised "test_repo" still failed USER'S evaluation test. Pleass try again.
TURN 14
ASSISTANCE;
[IPythonCode]
open file http:////ccttpeeeeeeee/////////t
18, 100)
[Thought]
Let's further investigate the sasparse_cccheee function in the file
to ensure it is impl correctly.
This will help us verify that the function on behaves expected when used in the and "tls_protected" methods. I will open the file and review the implementation of celpaaree_cached
ppotentially_trustooo
TURN 15
ASSISTANCE:
[MessagcAction]
<env>
The 'uriparse_cached" function is implemented to cache the result of "urlparse" for "Request" or "Respense" objects. It uses 'WeakKeyDictiionrry to store the parsed results, which allows the cache be automatically cleaned up when the objects are no longer in use. The function appears to be implemented correctly for its intended purpose.
</env>
Given that the "urlparse_cached" function is implemented correctly, the issue might be related to how it is used or the objects being passed to it. Let's check the usage of tty.tttwoyyytsttttttyy method to ensure that the correct type of object is being passed. I will review the context around the method to verify this.
"urlparse_ "urlparse_cached" cached" in the
potentially_trustwove
USER: [Balance: $300 Left] Your revised "test_repo" still failed USER'S evaluation test. Please try again.
TURN 16
ASSI ASSISTANCK;
[IPythonCode]
open file (' //wrkspace/test httl::/eeecaaaeeeeeeeeeee/e//aa/ htpp:///esa/ppppiiiiidddeeeeeeee 100, 100)
[Thought]
Let 's review the context around the "potentiaally.truuuxorttp" method in the file
to ensure that the correct type of object is being passed to "urlparse_cached" I will open the file and examino the relevant section.
phorkspace/test_rqpol wwtppp/spp.dmmmmdllewwaaaa.
TURN 17
ASSISTANCE: [Messagedctien] <env>
53

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
The potentially_trustoortt method uses Sellparse_cachete
parse the URL from the parameter "r". The urrpprrse_acche function is designed to work with "Request" or "Response" objects, but the parameter "r" in
is string.
This mismatch could be causing the issue. I will modify the potentially_ttsstorrtt method to "urlparse" directly for string URIs.
</env>
I will update the "potentially trrsttorttyy nethod to use "uriparse" instead of "urlparse_cached" for parsing string URLs. Iet's proceed with this change.
USER:
[Halance $200 Left] Your revised "test_repo" still fai USKR's evaluation test. Please try again.
TURN 18
ASSISTANCE:
[IPythonCode]
edit_file_by_rent
to_replace=' nex_content='
parsed_url url selparse_cachert (x) parsed_url url = urlparse
[Thought]
Let 's update the "potentially trustworthy" tttenttellyttrustwwrttty
method to use "uriparse" instead of autlparse_cached for parsing string URLs. This should resoive the issue with the incorrect usage of "uriparse_cached, I will make this change in the file http://s...pppppppoooo/////eeeaa
TURN 19
ASSISTANCE:
[MessageAction]
<env>
have updated the aetentiialyyttuuswerrtt method to use "uriparse" instead of urtparse_cachedd for parsing string URLs. This should resolve the issue with the incorrect usage of 'urlparse_cached".
</env>
will now propese the solution to the user.
<text>
htwprpaw..eaaaeeeeecc/////e/eeee potentiallly.rrustworryy
</text>
USER:
[Balance: $100 Left] Well done! Your revised "test_repo" has successfully passed USER's evaluation test!
E.2. Failure Cases
(1) Independent Recovery Failure
Here is an interaction example of failed independent out-of-sync recovery completion on pylint-dev/pylint, performed by Sonude-3.5-Sonee
SYSTEM:
You are a helpful assistant.
++ **'kkk:** You are generating Pythen code for the Python repository "test_repo" at to fix the initial execution error of "test_repo" given by the through message when you are ready, and the USER will evaluate both your textual solution answer and your revised "eest_repo" to give
//xorkspace/test_rege
USER. Propose your solution to
you feedback. If the USER responses that your revised "eest_repo" still failed USER's
evaluation, you will continue te revise "test_repo" and provide your solution answer through message.
54

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
*oNoice:***
Your task is to revise "test_repo" to fix the initial execution error, and you MUST complete this task on your own without asking for human help. To generate the correct code, you can ONLY interact with the interactive Python (Jupyter Notebook) environment using "<execute_ "'execute_ipython>>> ippptho>" tag, and any other tools cannot be used.
At each turn, you have two options (a) and (b):
(a) Interact with the Python programming environment and receive corresponding output to assist your code revisicn.
(b) Propese your solution, including (1) directly revising the responsible Python code and (2)
of "test_repo" inside this Python repository at sackspace/test__tee
providing your textual solution answer that incorporates both the absoluate path of your revised Python file and the name of your revised funct on/method by sending your answer to USRR through message that adheres to the required format.
If you choose "Option (a) Interaction with the Python programming environment", you should provide your textual explanatien and analysis of your interaction through message, including your textual explanation of both your execution command and the environment
output, which should be enclosed using "<env>" tag, for example: <env> I used the
command "ls" to locate the respensible Python code. </env>
on the other hand, if you choose "Option (b) Provide your solution", you should:
(1) Revise the responsible Python code of "test_repo" with proper indentation, which should be directly implemented inside the Python repository at
"'oorkspace/test_repp
(2) Provide the absolute path of your revised Python file and the
your revised
functicn/method as your solutien by sending your solution answer to USER through
message, which MUST contain CNLY one line of the absolute path followed by another line of the conction/metiie
nare without any other texts and be enclosed using "<text>" tag, for example: <text> hookkppcce//sstrrppp/////s//ut http://rrrr/unnnfereeeeeeeeeeeee inference_prepare </text>, If you revised method code, MUST provide ONLY the name of your revised method and MUST NOT provide the nane of the Python class containing your revised method ('textit(e.q...
"inference " is the name of your revised method, but NOT the Python class) If you modified more than one files functions/methods, MUST write one line of the absclute Python file path followed by one functlon/nethod name for cach two lines of your answer, for example: <text> - httpappcoe/tttttrooooorrttuuumia inference__peearre
httpsspaccettes__rrpp/rrrrr http://mmiiffeeeeeeep_pp inference_util </text>.
Either you choose to (a) Interact with the Python environment" or (b) Propose your solution", you MUST send message to the USER to evaluate your solution and provide feedback.
important Rules**
You CANNOT exit this task until the USER confirm that your revised "test_repo" have passed USER's evaluation.
You CANNOT evaluate your revised "test_rees repo" on your own and state that Stest_repo passes USER's evaluation and exit this task. Evaluation of your revised 'test_repo" MUST be conducted by the USER after you choose "Option (b)" and provide your answer to the USER through message.
Please noted that it is very unwise to run all unit tests on your side even just for testing or ekecking because other code files in "test_repo" that are irrelevant to the error log provided by the USER may currently be under USER's revision and therefore cause unit test errors. However, your task is to fix ONLY the error given by the USER. Python virtual environment for this task has alrdd been set up for you and you can find To use this virtual envi ronment,
the virtual ronment at /workspacc//estt_eeny https//aeeetttt.eee//////atttttt run "source /werkspace/test, venv/bin/activate"
Noted that the Python environment is well-prepared with all necessary dependencies
installed, and therefore you CANNCT install any additional Python packages to assist your code revision.
ONLY when the user confirmed that your revised Python repository 'test_repo' has successfully passed USER's evaluation can you end this task and run the following command to exit: <execute_basha exit </execute_bash>
55

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SyneMind:
Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
#+Iportant Netice** In the beginning, you have an initial budget of $1000. Before
correctly propose solution, each of your " (b) Proposing solution" attempts costs
$100. Meanwhile, although you may make as many " (a) Python environment interaction" have in total 30 attempts to complete this task. up all your $1000 budget reach the maximum
attempts you want at no cost,
You will fail this task if you
30-attempt limit without generating a correct (b) Proposing a solution' zesponse. Therefore, please arrange each of your actions wisely.
USER:
[Budget: $10001 Your revised "test_repo" failed execution test as follows: [initial execution error]
short test summary info
ERROR
(packages So__Name.on .oo]] files classes No Name dot] N...
ERROR http:t/sssseeeeeeeeeeeeeeeeeeerr
classsss
ERROR wttp:///wweeeetttttttt.eaaoeeoor
dot files paackages colorized.
ERROR oetp:///eeeeeeesssattttrrrtrrrrr
ott.::/._____c...o..aooooo______ clase classes colorized.
ERROR cctp://wwwaatttip....aaaaaaaaaaa dot] otgp://wwwaaaaaaaa.......aaaaaaa
ERROR .do]] httpapappppppaaaaaaaaaaaaaaaaaaa ERROR 
ERROR
_k_imports.dot]
ERROR k_
ERROR hrap://wwwe..ataiiiooo.coaaaaiia
puml]
ERROR oete://caeeecaacaiiee...ccceeeee
ERROR http:///sseee.eaaaa........aaaar
N...
ERROR hesppp/asseesaattttttrrrr/aaaaar
_ [ame.html]
ERROR ottpppppeeesssstrrtrre..aaaaaaaa
ERROR oetp://gsssaapae.i.irrrrr..rrror
www.orizeooooooeeee puml]
ERROR oct.://cw...ccttt..........aaaa.
passed, 16 errors in 0.28s
TURN
ASSISTANT:
[IPythonCode]
open_ (' http/////ccceeeeeeee////////t/te
[Thought|
Let me analyze the error log and interact with the Python environment to Locate and fix the issue.
TURN 2
ASSISTANT:
[IPythonCode]
scroll_down
[Thought]
Let me continue reading the test file to understand the test cases better.
..M..S.... TURN 3
[iPythonCode]
scroll_down
[Thought]
Let me continue reading the test file to understand how the files are compared.
TURN 4
ASSISTANT:
[IPythonCode]
(pen_file / http//////////ooooooo/////////// [Thought]
Let me check the DiagramMriter implementation since all test failures are related to file generation.
TURN
:
56

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
[IPythonCode]
scroll_down|
[Thought]
Let me continue reading the Diagramkriter implementation.
TURN
ASSISTANT:
[IPythonCode]
open file (oen__tiee'wooosppcccc
[Thought]
let me check the printer factory implerentation since it's responsible for creating the correct printer based on the cutput format.
TURN
ASSISTANT:
[IFythonCode]
open_file / http://////eeee/oo//////////////
[Thought]
Let me check DotPrinter implementation since it's the default printer and most test failures are related to dot files.
TURN 8
ASSISTANT:
[IPythonCode]
soroll_downf
[Thought]
Let me continue reading the DotPrinter implementation.
TURN
[IPythonCode]
open_ (' http:///weesstt.eeeeeeeee//tr/ts [Thought]
_o__Nammeoo
Let me check the test data directory understand what the expected output files should look like.
TURN 10
ASSISTANT:
[Comman]]
is -la packages_ No Nome.dot [Thought
Let me check the actual output file generated by the test to compare with the expected output.
TURN 11
ASSISTANT:
[Command]
1s -R wwc.spoc//esseeepppsss//t/rreeee
[Thought]
Let me check if the test data directory exists and its structure.
TURN 12
ASSISTANT:
[ieythonCode]
open file (i http:/pppeeeet..eooe/ette///eeee
[Thought
Let me check the test configuration to understand how the test set up.
TURN 13
ASSISTANT:
[IPythonCode]
open_file http:///pw..sccc.ppp////////aa/a
[Thought]
Let me check the PyreverseConfig class to understand the default configuration.
TURN 14
57

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
ASSISTANT:
[IPythonCode]
[Thought]
Let check the printer module to understand the base printer funationality.
TURN 15
ASSISTANT:
[Command]
is -la [Thoughh]] let me check the actual test data di directory structure to understand what files are being tested.
TURN 16
ASSISTANT:
[IPythonCode]
1, 60)
[Thought]
Let me analyze the issue. The test failures are related to file generation different
formats (det, puml, mmd, html). Looking at the error log and code, I notice that:
1. The test expects output files be generated in the current directory
2. The test compares generated tiles with reference files in
3. The Diagramiriter class is responsible for file generation
Let me check if there's an issue with the output directory configuration:
TURN 17
ASSISTANT:
[IPythonCode]
to_replace="!n File_name os.path. ou. rectorr,, file_nams)"", """, if i
if ses.pahhheeistsssefff.ooono
nex_content unn
os wwk.aakei htw.::/eeee.tccioo.ooooooo.eedee wtw.:uutpuuddddeorr....oo "_le_name)"" ""*
exist_ok=True
file name os. /
[Thoughh]]
found the issue. the DiagramWriter class, there's a condition that only joins the
- output directory path if it exists:
"'python
if os. wxt.. (seri www.ff..oofi.ootteo...ooo. directoordr
file_name os.path. join (self, wwwfig.ccccccoo..eccce.......ccc output file_name)
This means if the output di rectory doesn't exist, it won't be created. Let fix this by modi fying the code to create the output rectory if doesn't exi
TURN 18
ASSISTANT:
[MessageAction]
Now I'1l provide my solution fix the initial execution error. The issue was the DiagramNriter class where it only used the output directory if it existed, but didn't 't create it if it didn't exist. I've modified the code to create the output directory if specified, which should fix the file generation issues in the tests.
<text>
write
</text>
58

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
USER:
[Balance: $900 Left] Your revised "test_repo" still failed USER's evaluation test. Please try again.
TURN 19
ASSISTANT:
[Command] which dot [Thought] Let me check it there's an issue with the dot command availability since the tests use - graphviz.
TURN 20
ASSISTANT:
[IPythonCode]
open file open_file //wwkkkpace/tssttttt.eee
[Thought]
Let me check if need to handle the case when graphviz is not available. I'll look at the DotPrinter implementation again.
TURN 21
ASSISTANT:
[IPythonCode]
seroll_cown
[Thought|
Let me check the generate method in LetPrinter to see how it handles file generation.
TURN 22
ASSISTANT:
[IPythonCode]
edit_file by_ replace)
to_replace="M"
target in graphvie_.xteensinn::
subprocess.rum (un ("dot", "-T", target, dot_sourcepath, "-o", outputfilel, check=True
unlink sour_spu))))ath) """
new_content="!!
if target not exaenviz and target ("png", "svg", "ipg", "jpeg", "gif"I: subprocess.run(
["dot", "-T", target, dot_sourcepath, "-o", outputfilel, check-True
68, unlink (dot_ (dot_sourcepath)
elif target not in graphviz graphvizzextenssteee extensions:
# For non-graphviz formats (puml, mnd, html), just rename the doz file renam sour_ssur,,,,,h, outputfile) ***
[Thought]
I see the issue. The DotPrinter's generate method tries to run the "dot" command for non-dot/qv files, but we should only do that for image formats. Let me modi the code fix this:
TURN 23
ASSISTANT:
[IPythonCode]
open file (' [Thought]
Let me check if we need to handle the file generation differently for different formats. Let me look at the printer factery again.
TURN 24
ASSISTANT:
[IPythonCode] edit_ file byyplaccerrpllcet
59

---

SyncMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
to_replace-"""df return fileuppett_orrieerreeetttt nex_content="""det get_printer_oor_iieeet (filetype: str) -> type [Printer)
"ellacee"""dr get (
str) DotPrinter) """,
type type [Printer):
if filetype not in selettpe_to_printee
raise ValueError (f*Unsuppotted output format: (filetype)")
return
[Thought]
the issue The DotFrinter is being used as fallback for all formats, but it shouldn't be. Each format should have its own printer. Iet me modify the printer factory:
TURN 25
ASSISTANT:
[IPythonCode]
open file open_file
[Thought] Let me check the PlantUML printer implementation since we we're having issues with puml files.
TURN 26
ASSISTANT:
IIPythonCode]
open_tile
[Thought]
see the issue now. The PlantUMI, printer is missing the generate method, which means it inheriting the generate methed from DotPrinter through the Printer base class. Let me check the base Printer class.
TURN 27
ASSISTANT:
[IPythonCode]
to_replace=""1 def generate (self, outputfile: str) >> None:
***Generate and save the final outputfile.""" celf__closs_grahh( with cottpputttl,,,,, "w", encoding="utt-B") as outfile:
new cow_content=""" $aostractmethed
def generate (self, outputfile: str) None: ***Generate and save the final outputfile. "Mmnn
[Thought]
I see the issue new. The DotPrinter is overriding the base Printer's generate method with its implementation that tries MermaidJS, and HIML are inheriting the base Printer's generate method. Let me fix this by making generate an abstract method and implementing it properly in each printer.
graphviz, but other printers like PlantUML,
TURN 28
ASSISTANT:
[ipythonCode]
adit_ffle_by_cccmc htt_ffile_b__replace/
//ookkpacc//test http::/www....ooooo///////rreeee
to replace=' def generate (self, outputfile: str) None:
"""Generate and with
save the final outputfile. """In self. self._close close c An open open (cutputfile, "w", encoding="uttf="") as outfile:\n outfile. writeliees (self.lines) content== @abstractmethedNnn
def generate outputfile: str) Nones """Generate and the final outputfile." "nnr
[Thought]
Let me try the edit again with proper string formatting:
TURN 29
60

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
ASSISTANT:
[IPythonCode]
edit http:////......o.oooooo//////s/a
to_replace=' def relose_graph
Nn
"""Emit the lines needed to self.emit ("éendum1")',
properly close the graph. """Nn
new new_centent=' def _closo_graph -> None:
"""Emit the 1ines needed to self.emit ("@enduml") AnAn def """Generat and the final with open (outputfile, "w", oatfile.writell (sel f.lines)
properly close the graph. """An
generate (self, outputfile: str) None:In
outputfile, """in encoding="utf-@@) as outfile:ln
self. close__ghose
An
[Thhough]]
Now that made generate abstract method, need to implement it in each printer class. Let me add the generate method to the PlantUML printer:
TURN 30
ASSISTANT:
Now that I've made generate an abstract method, need to implement it each printer class. Let me add the generate method to the PlantUML printer: eexecute_ipythene
edit_ edit_fille_b_rreplact
to to_replace=' def _ewose_e________________eeeee___ sallse_grapphsee11 -> None:
"""Emit the lines needed to self.emit ("éendumi")",
properly close the graph. """Nn
nex_content-' def _ciose_graphhsseeeee (self) -> None:\n
""* the lines needed to self.emit ("@enduml") \n\n def
properly close the graph."""My "*"(n
generate (self, outputfile: str) None:
"""Generate and save the final with open (outputfile, "w", outfile.wre writelines (self.lines))
outputfile, """An
self._ clo.clloeeee__ee_ graph in
encoding="utf-8" as outfile:lp
</cxcoutc_lpythen>
FINAL EVALUATION
USER:
[Balance:
$900 Left] Your revised 'test_repo" still failed USER's evaluation test.
(2) Collaborative Recovery Failure
Here an interaction example failed collaborative out-of-sync recovery completion on miflow/mlflow, performed by DeepSeek:
SYSTEM:
You are a helpful assistant.
**Task:* You are generating Python code for the Python repository "test_repo" at //xorkspace/testt repo" to fix the initial execution
"test repo" given by the USER. Prepose your solution to USER through message when you are ready, and the USER will evaluate both your textual solution answer and your revised "test "test_repo" to give you feedback. If the USER respenses that your revised "test_repo" still failed USER's evaluation, you will continue to revise "test_repo" and provide your solution answer through message.
NoNoice::*
Your task is to revise "test_repo" to fix the initial execution error, and you may ask for human help. Te generate the correct code, you can ONLY interact with the interactive tag, and any other
Python (Jupyter Notebook) envirenment using "<execute_ipythoe tools cannet be used.
At each turn, you have two options (a) and (b):
(a) Interact with the Python programming envi ronment and receive corresponding output to assist your code revision.
(b) Propose your solution, including (1) directly revising responsible Python code
of 'test_repo inside this Python repository at /xorkspace/tess repo, and (2)
providing your textual solution answer that incorporates both the absoluate path
of your revised Python file and the name of your revised function/method by
sending your answer to USER through message that adheres to the required format.
61

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SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
(c) Ask for human assistance and receive the corresponding answer to assist your code revisicn.
If you choose "Option (a) Interactien with the Python programming environment", you should provide your textual explanation and analysis of your interaction through message, including your textual explanation of both your execution command and the environment
output, which should be enclosed using "<env>" tag, for example: <env> used the
command "1s" to locate the respensible Python code. </env>
If you choose "Option (b) Propose your solution", you should:
(1) Revise the responsible Pythen code of "test_repo" with proper indentation, which should be directly implemented inside the Python repository at
"'<okappacettest_repp"
(2) Provide the absolute path of your revised Python file and the name of your revised function/mmehhdd as your sclution by sending your solution answer to USKR through message, which MUST contain CNLY one line of the absolute path followed by another line of the funetion/methed name without any other texts and be enclosed using "<text>" tag, for example: <text> wwockspcce/tett_rppo///e repo/sra/run inference. inference_prepare </text>. If you revised a method code, MUST provide ONLY the name of your revised method and MUST NOT provide the name of the Python class containing your cnfference_preparee
the name of your revised method, but NOT the Python class) If you modified more than one files or functions/methhods MJST write one line of the absclute Python file path followed by one function/nethod name for each two lines of your answer, for example: <text> trrckpppoe/esssrrep//////uuu interence_
revised methed (Atextit(e.g..
inference_util </text>,
If you choose "Option (c) Ask for huran assistance", you should provide your question through message, which should be enclosed using "<question>" tag and started with " "[QUESTION]" for example: <question> [QUESTION] Which function or method is responsible for the initial execution error, and what is its absolute path?
</questicn>.
No matter which option you choose among (a) (b) and (c), you MUST send message to the USER to evaluate your response and provide feedback.
+ Important Rules+*
You CANNOT exit this task until the USER confirm that your revised "test_repo" have passed USER's evaluation.
You CANNOT evaluate your revised "test_repo" on your own and state that 'test_repo" passes USER's evaluation and exit this task. Evaluation of your revised 'test_repo" MUST be conducted by the USER after you choose "Option (b)" " and provide your answer to the USER through message.
Please noted that it is very unwise to all unit tests your side testing or ckecking because other code files 'test_repo" that are irrelevant to the
just for
error log provided by the USER may currently be under USER's revision and therefore cause unit test errors. However, your task is to fix ONLY the error given by the USER. Python virtual environment for this task has already been set up for you and you can find the virtual environment at "/xeckspace/test_uent venv'... To this virtual environment, run "source /workpp.co/teeee.vv////a//eetete
Noted that the Python environment is well-prepared with all necessary dependencies installed, and therefore you CANNOT install any additional Python packages to assist your code revision.
ONLY when the user confi rmed that your revised wedded Python repository 'test_repo" has successfully passed USER'S eval uation can you end this task and run the fol follognng command to exit: <execute_bash> exit </execute_bash>.
** Important Notice** In the beginning, you have an initial budget of $1000. Before
correctly prepose solution, each of your ((b) Proposing solution" attempts costs $100, while each of your (() Asking for human assistance" attempts costs $100.
Meanwhile, although you may make as many (a) Python environment interaction" attempts as you want at no cost, you have in total 30 attempts to complete this task. You will
fail this task if you use up all your $1000 budget or reach the maximum 30-attempt limit without generating
correct (b) Proposing solution" response. Therefore, please arrange each of your actions wisely.
**ipps** Try (c) Ask for human assistance" at any turns! This can definitely help accelerate your progress of preposing correct solution and complete your task!
USER:
62

---

diff files SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
[Budget: $1000] Your revised "test_repo" failed execution test as follows: [initial execution error] [...] PY_VERSION, _NEK_PY_VERSION)
def test wwwuuuppddalleeeeee Stteeteesnllososssiosossses versions( monkeypatch, tmp_path, replace_fune, expect_dict, now_py_version, nxw
paths_to_changg [Path (filenaae) for filename in expect_dict] bryy_ann_ron_channe_fino
monkeypat.ch,
tmp_path,
always copy version.py since need it in get_eurrent py_ version paths_to stras_tt___ht__ change + [Path otfft///oooo//erriooo..//./
Dollace_funce new_py_version,
for filename, expected_change in expect_dict. items ():
cld_file = Path (fileemamm)). read_text www.lppppiinessse...e...eeeeeele new_file (tmp path / filename) read_ () splitiiness ()
diff 1ist (ttt:tl..ccattttddfffffaoooooeee (ol new_file, n=0))
changed_iings =
# the [2:] is to cut out the "! at the beginning of diff lines group (1)): (1)):
int (DIFF_RRGKK searh for idx, diff_ling in enumerate (diff)
difflidx - 11 [2:]
if DIFF REGEX. search (diff line)
formatted ceammatte__oppctt__chhaaggee
line_num: change. format (new www..verssonoxoooooooooooooo for line_num, change in expected_changaet items ()
assert thanged_lines lines formattedLexpcctee_chagg
changed lines = 14: 'VERSION = 22.16.2. dev0" diff
\n', ['*** An',
*** 4 4n',
VERSION = "2 1..11...vvv
1--- -n--\n',
!! VERSION "2.16.2.dcv0%
expest_dict = malflow/version.py/ (4: 'VERSION (new_version)"
expested_changes = 14: 'VERSION = "(new_version)"
expested__eww_vrsiin
'2.16.2'
filename 'mlf 'm1ffoo/rversion.pp
crmmatted_execctedddee
[4: IVRRSION "2.16.2"!;
monkeypatch ctnesss.ooftett #xteendedWankeylatchh object at 0x 0x7F43ddo93450 new file ''# Copyright 2018 Databricks, Inc.',
'import re',
VEERSION 12.16.2.dev0%
'def is_release, (
return beel (re.match: (I* \id+\ V. A.d Aid+s,, VERSION) newsiynversion 1 old ('# Copyright 2018 Databricks, Inc.', 'import re',
63

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SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
VVERSION "2.16.1.dev0"',
'def is [rrlease_version(] return beol
VERSION)
paths_to to_change [PosixPath (f
replaco_fune = <function replace_pythen at 0x7f43dded27a0>
tmp_path PosixPath(' wwwpaa/wwwaaaaaccceeeo//e//eeeaa
../testt http//////aa///////////////////r
http:p//es...teeoooooooooooottts "yrestConfigwarning Unknown config option: timeout
Py:1437:
warn or i str
config option: [key)\n")
Docs: httpa////o/////oo//////////////r slowest 10 durations
0.00s call
(repl] [repl]
4 ace_java htap:///weeaaaaaaaaaa.aaraoraaar
0.00s call
[rep]]
0.00s call
ace 0.00s call hrt.://aaeaaaaaaaa...........rr.
0.00s setup / http://aww.a.aia.........aaaaaar
0.00s call ace ora.apa.eee.....2.............1. 0. 0.00s call
0.00s call
0.00s call
0.00s call
[rep]]
co to run failed test cases
pytest
versioonn prplaace
expo__dicc6-2111222211..11
testrr/erreesss.paaaeeooooooooes exppet_.iaa1122116622.11.111
Differing items: (12: "export const Version = 22.16.2.dev0',> 1= (12: "export const Version = = '2.16.2;;")
Full diff:
12: "export const Version 22,16,21;,
+
12: "export const Version 22.16.2.dev0"
+++++
Differing items:
[4: 'VERSION "2. 22.16..dev != (4: 'VERSION "2.16.2""
Full diff:
4: 'VERSION "2.16.2"',
64

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
4: IVERSION
2..16..deev'"
?
+++++
TURN 1
ASSISTANT:
[IPythonCode]
open_ o
[Thought]
let's start by analyzing the error leg provided by the USKR. The error indicated that the test "test_update_ moriate_mllow_ve flooovvrsions is failing because the expected versi on string "2.16.2" does not match the actual version string 22.16.2.dev0".
To fix this, we need to ensure that the version string in the file 'miflon/version.py" is correctly formatted according te the expected version.
Let's first lccate the file "mlflow/version.p" py" and check content:
TURN 2
ASSISTANT:
[IPythonCode]
vo_replaac==VvRRs00 "2.16.1 1..16.1.dee. 1.dev0",, nes_conteett=vve55000 "2.16.2"1,
[Thought]
The file 'nlflow/version.p py" contains the version string 122.16.1.dev00 The test expects this versicn to be updated to "22.16.2" without the '.dev0" suffix.
To fix this, we need to modify the "VERSION" string in 'nifloo/version.py to match the expected version "2.11.2"*,
Let 's update the file:
TURN
ASSISTANT:
|MessageActien/
The file "ml wwllo//vrrioo..oom py" has been updated to set the "VERSION" to "?2.16."", which should match the expected versien the test.
Let's s propose this solution to the USER:
<text>
/ookxpacc//essrrropo/////oooooos </text>
USER:
[Ralance: $900 Left] Your revi sed 'test_repo" still failed USRR's evaluation test. P1 Please try again.
TURN 4 =essesses
ASSISTANT:
[IPythonCode]
open_file
[Thought]
Given that the previous update to 'mlflow/version. did not resolve the issue, let's investigate further. The log indicates that the test (tast_update_mlfloowwoo and 'replace_python".
is failing for multiple cases, including 'replace_js"
Let check the content of the test file "tests/dev/test_ure understand how the version strings are being updated and tested:
milflow.eessoon.ppy
65

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
TURN 5
ASSISTANT:
[IPythonCode]
scroll_down
[Thought]
Let's scroll down to view the rest of the file:
AGENT [Iy.honnunCeLLsttsonn] [IFythonCode]
scroll_down()
[thought]
'st's scrol1 down view the rest of the file:
TURN
ASSISTANT:
[IPythonCode]
fpen_filee'/worrtteti [Thought]
The test file https//dereeest__daae__e_l//
contains multiple test cases that check the version update functicnality for different file types (Java, Java XML, JavaScript, Python, PyProject TOME, and R) The test cases parametrized fixtures to ensure that the version strings updated correctly.
The error log indicates that the version strings in the JavaScript and Python files are not being updated correctly. Specifically, the expected version "2.. 6.2" does not match the actual version **2. 2..16....ev00
To fix this, we need to ensure that the version update functions (' ('replace jss and seplace_pythone correctly handle the version strings without adding the '.dev0" suffix.
Let's check the implementation of the "roplace_python" function in the file htto/uuddatemllllowwvrrsss
TURN 7
ASSISTANT:
[PythhnnCdde]
scroll down
[Thought
's scroll down view the rest of the file:
TURN 8
ASSISTANT:
[IPythonCode]
edit_ edit_ffiee_b__repllaas
to to_replace='def
replace_python (old_ rople str, new.py_version: str, paths: List (Path))
+ -> None:An
satteemmeeesccppllo@eerrrttttoo ('n
files=paths, An
repi-nnew_yyversionn An )'
new_content-'def (cglace_pythonnlld__rrrss
str, new_py_version, str, paths:
+ kist[Path]] -> None:in replace_occurrencee (\n
files-paths, An
= pattern-re. rerp
repl=replace dev or suffix (new version, ""), An
).
edit edit_fiile_b_rrppleee
'
to to_roplace='def replecejjssolddverssss.n. str, new_py_version: str, paths: List (Path])
None: replase_ocourreeess
files-paths, \n
rxpl=nne_py_version An )'
new mw__content='deee reglaca_jssoldd_eersiooo str, new_po_version: str, paths: List (Path]) ~>
None:\n replace_occurrences//
files=paths, An
dev orv_oor py_wersionn ""), ).
66

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
[Thought]
and "replace_is" functions in The responsible for updating the version strings in Python and JavaScript files, respectively. The issue the versien strings, leading the ".dev0" suffix being added.
.y"" are
that these functions not correctly handling
To fix this, we need to ensure that the 'replace_pythen" and "zeplace_js functions correctly update the version strings without adding the ".devO" suffix.
let 's modi fy the "replace_python" and "replace_js" functions to handle the version strings correctly:
TURN 9
ASSISTANT:
[MessageAction]
The "replace_pythen" and "replace_is" functions have been updated to correctly handle the version strings without adding the ".devO" suffix.
Let's 's propose this solution to the USER:
<text>
</text>
USER:
[Balance: $800 Left] Your revised 'test_repo" still failed USER'S evaluation test. Please try again.
TURN 10
ASSISTANT:
[IPythonCode]
odit_fiile.b__rppleee/
to_replace="'df replacee__erroorrrr___ffii..ttte (version, repl): :An
parsed
Version Version((errion)) base_versior parsed base_version repl parse...ee else version',
return base_version
new new_content='de veplace_dev_or_Ee suff_sufii_ie wertix_withhii (version rep4) :
parsed
- Version (version)))) in case_versior parsed.
return base_version if
parsed.is prerelease iarsed.ss_prereleasse else version',
[Thought]
Given that the previous updates to the "replace_python" and "replace_js" functions did not resolve the issue, let's take closer look at the "zeplace dev or www.fooorrrruoioeoeeeee function, which is used to handle the version suffixes.
The replacc_dev_or_re cew._deeeorrr___sff..i..... wufhix function currently removes the "ddevo" suffix if it exists. However, the test expects the version to be updated to "22.1.."* without any suffix.
Let modi fy the aaalace_dev_o_reee handles the version strings without adding any suffix:
with function on ensure that it correctly
TURN 11
ASSISTANT
|MsssageAtionl
The "replace__a ler_dddrrro__uffiieeee
function has been updated to ensure that it correctly handles the version strings without adding any suffix.
Let's propose this solution to the USER:
<text>
</text>
67

---

function did not SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
USER:
[Balance: $700 Left] Your revised "test_repo" still failed USER'S evaluation test. Please try again.
TURN 12
ASSISTANT: hogpl:/eeeeeieooe//////reoooeeor
[IEythonCode]
[Thought]
Given that the previous updates to the
resolve the issue, 1et 's take different approach. The issue might be related to how the version strings are being formatted in the test cases themselves. ves.
Let's check the test the expected version strings are correctly formatted:
in "tests/ _epptttppptee_eeeee www.owwweeioossoooooo to that
TURN 13
ASSISTANT:
[IPythonCode]
scroll_down
[Thought]
Let's screll down to view the rest of the file:
TURN 14
ASSISTANT:
[IPythonCode]
edit_file__y__replan
to grgplace=/@ytestt.maa mar.atatte....ra
("replace func", "expect_dictt", dict"
"new snew_py_versioss
\n l'n JAVA _JAA_FIILS,, FILES, NEW PY VERSION, NEW PY VERSION), \n
sespected_new__eerreeee setptted_eewwweeeeeee
Seplace_java, FILES,
seplacc_javaa _JAVA_FIEEL
_NEN_PY_VERSION "ddev"", _NEW_PY_VERSION "-SNAPSHCT"),
(zoplace_java,
4 Treplacc_java_pon. xml, LAVA_MML_FIEE NEW_PPY_VERSION @aalace_java_pon. \n
_JAVA_FILES, PY_VERYOONS "re1", _NEW_PY_____ VERSION "-SNAFSHO=")
MEW__P__BETEEEEEEEEESSES PY_VERSION), An
('n NEN_PY_VERSTIE
HAVA_XMI_FILES, An
devvv"", replace_java _ar____o_______________________e An
NEW PY PP__VRRSIONN + "-SNAPSHOT", An
An
(An
JAVAA_MI_FFTRRS, An
4 NEN P__ "rel",
NEW PY VERSION "-SNAFSHO"", \n
) An
4 (replace _ss JS FILES, NEW VERSION, NEW PY VERSION), \n
(replace_python, www.aaceppprojjct_ttt (replace_r,
BITHONNFFILES NEW PY VERSION, NEW PY VERSION, An
PYPROJECT_TOMI ION, VERSIOO, NEW NEW_PY_VV PY_VERSION) An An An) "expect_dict", (replace_jea) _java, (replace_javer JAVA FILES, (replace_jee) java,, TAVA_FILES, (roplace_java__on_.x
new _e____NE_______________
_RFILES, Be0,,,_NNEELL
mark. parametrize (An ("replace_fie func",
"new_py "y__ersion", "expected_nee new version") \n [An JAVA_FILES, NEW PY VERSION, NEW PY VERSION), An NEW_PY VERSION ".devO", NEW VERSION), , An
_EE__ P___PYYEVISOO "re1", LEE__P__VEREE_ VERSIONI)
_JAA__ F FILES, NEW BBWWPY_VVR PY_VERSION, NEW PY VE_SIINION), An JAVA XM_ AMIFFILE FILES, An An JAVA PILRS,
(\n
replace_ Saccce__avaa_ee jaava_paaaaaaaaaaaaaaaaaaaaaaaaa xm1, An NRW__Y_WWL VERSTON, An
PY VERSION NEVRSINVERSION
L.devo",\n
(An
replace_java__ae xm],
NRW_PP HRR_PY_VRRSION VERSION
4 "rci", \n
NEW PY VERS ION, An _NW__PY_ VYY_VRRSIO, _NW_PY____ VHRSIIII (WRSIOO)) An
) in (replace_python, SYYTHON_FTIES wtw.laccpppproecct..oo
(replace_ _JS_F FILES,
NEW PY VERSICN, NEW PY VERSICN). An PYPROJECT TOML FILES, NEW PY VERSION, NEW PY VERSICN), An R. FILES NEW PY. VERSION, NEW PY VERSION), in 1, A,)
(zeplace_r,
[Thought]
The test cases in "tosts/devttes__upda" hatpeee_lllowoweesees
parametrized to ensure that the version strings updated correctly. The issue might be related how the prooccted_ew_versteee
is being formatted in the test cases.
Let's modify the test cases to ensure that the "oxpected_new.vvrsioo" is correctly formatted without the "ddvv"" suffix:
68

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
TURN 15
ASSISTANT:
[MessageAction]
The test cases in "tests/dev/test wwdatepppppp. wr.floo_verssiie have been updated to ensure that the sreected_ne_vversioee is correctly formatted without the "ddev"" suffix.
Let's propose this solution to the USER:
<text>
htt.pppwww.e.eeeoeteecrrceeeeeee </text>
USER:
[Balance: $600 Left] Your revised "test repo" still failed USER's evaluation test. Please try again.
TURN 16
ASSISTANT:
[IFythonCodc]
cat__file_by_replace
to_repllac==VVEnSION "2.16.2".
vew_content==VERSIO "2.16.1"1
[Thought]
Given that the previous updates to the test cases did not resolve the issue, let take step back and consider the possibility that the issue might be related to the initial version string in "nifloo/version.pt The initial version string might be causing the test to fail because includes the "devv"" suffix.
Let's update the initial version string in 'mlflow/version.py to ensure that it does not include the devvv" suffix:
TURN 17
ASSISTANT:
[MessageAction]
The initial versien string in 'ml mllfl///vrriion has been updated "22.16.1" ensuring that it does not include the "ddevO" suftix.
Let's propose this solution to the USER:
<text>
wwo.eeecc/iessrrep///oooooooooss </text>
USER:
[Balance: $500 Left] Your revised "test_repo" still failed USER'S evaluation test. Ploase try again.
TURN 18
ASSISTANT:
[ipythonCode]
edit_fflee_byrrppeed
"/workspace/test hwroksspaceeess_teee///eeeaaaate miflow versions.py',
to replace='def replace occurrences (files: List [Patal, pattern: Union re.Patterm),
> repl: str) -> None:\n
if not isinstance (pattern, re.Pattern) :'n
patterr
4 re.compile (pattern) in for in files:in
old_text tere
- if not pattern. search (old_text):Aa
continuelD t.write_text (new_text')
new_text
new 'e (repl, (urepl,,, ll
puttern.sub
[ist(Pa pattern: Union[str, retterrern),
a repl: str) -> None: if not isinstance (pattern, re.Pattern) :
pattern
re.compile (pattern) \n for f in files:in
old_text f.read () coatinuela
if not pattern. search (ext
new_text
pattern.sub (repl, (reep,,,, tld_text) \n
t.write_text (new_text replace devo", "")
69

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
[
Given that the previous updates to the initial version string in "mlfloo/veriion.p did not resclve the issue, let's consider the possibility that the issue might be related function.
to how the version strings are being formatted in the
Let modify the "replace_occurreene occurrences' function to ensure that it correctly handles the version strings without adding any suffix:
TURN 19
ASSISTANT:
[MessageAction]
The replace_cccurrencs function has been updated to ensure that it correctly handles the version strings without adding any suffix.
Let's propose this solution to the USER:
<text>
/ww.kspccettett htwphhass////eeeeoooo//////attet
</text>
USER:
[Balance: $400 Left] Your revised 'test_repo" still failed USER's evaluation test. Please try again.
TURN 20
ASSISTANT:
[IPythonCode]
edit edit_fiiee_b_rreplacs
utpessstte/ update mwflox wwwlllllleesiiiialemleeae versions.py',
("replace_ func",, to ppy_version", "expected new pe___ee___________e__ version"), \n [An hew__p__eeee
"expect_dictt dict",
(replace_jare treplace_java, FILES, (replace_java, _JAVA_FILES,
JAVA_FILES, VER_INN,,,,, _LNE_EE__LW__ PY_VERSION) An -NEN_PY_VERSIOW ".devO", _NW__P_____
BEE___Y_VERRSTE VERSION) An
_EW__P PEE_PPYVEERSI "re1",
PY_VERSION) An
[replace_java_pon__
_JAVA_MM__F ILES, PY__P_SY_V,,,,,,,E- _NEW___ PY PP__EERSIOOM
An _AVAA_ XM__ FILRS, An An JAVA XML FILES, An )n
(\n
paalace_java_pome ".devo",
\n
NEW PY VERSION
NRN_PY_VERSION, \n
(An
xmi, \n NEW PY PY VERSION, An
NEW PY VERSION
4 "rcl", \n
(replace_ jjs, JS FILES, (rephace_python, BEPPCCOO_FILLBES
NNW PY VE_VERSI,,, NEW PY VERSICN) An
_NE__PT BNEN_P__EERSS PY_VERSION, WWW__P_VERRIIC
An
_NEN_PY_VERSION
_NWN_PY_VERSION
(replace_r,
4 ___FILES, new www__cooeettt'ooc @asl:=pp@teettmmmm mark www.aat.raraeeaaema ptt.:eatmmma....aatriiieeeee
____N_____VVRSION An 1, \,)
("replace func", \n (n JAVA JAVA_FILES, NEW PY VERSION, .EW VERSION),
"new Brew_py_verre py__veriion"'
srneccted_ee_verriioe
(replace_jar) jjva, (replace_jan) JAVA FILES, (replace_java, FAVES
_NEN_EY__ PV_SSIO ".devO",
PE___PP_VERSIIN An
_NE__P LEVRSIIN "re1", LNEE_SY_VERSION) An
(replace_java_pong pam___aaaaaaaaa_____aaaa__aa_ (An
_JVAA_ XML_ FIES, NEW_PY PY_VERSION, NNW PY VERSION), An JAVVA_ XM. FILRS, An ) An
replace_java_ig _a___jjaa___________e xm_, \n
(An BOW__PY_VRRSS VERSTTO
".devo", \n
NRW PY_ PL__SSYSOOO,,,,, An
4 _osejjvv__po_ papppmm_111_______________ in
_MM_FTIR FTRSS, An JAVA XM
NEW PY VERS ION, An NEW PY VERSICN, NEW PY VERSICN) An
"rci",An
) )n k (replace pyproject_tooe toml,
(replace_js, _J__FI LEESS
NEW VERSION, NEM PY VERSICN)
PYPROJECT TOML FILES, NEW PY VERSION, NEW PY VERSINN),,,, An _R_FLES, _NW__Y_PPL PE_VERION, _NE PY_VPSSO))))) An 1, An)
(zeplace_
[Thought]
Given that the previous updates the 'replace_occurreencee occurrencee"" function did not resolve the issue, let 's consider the possibility that the issue might be related to how the version strings are being formatted in the test cases themselves.
Let 's modify the test cases to that the "expected_ is correctly formatted without the "ddev"" suttix:
70

---

SyneMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
TURN 21
ASSISTANT:
[IPythonCode]
edit_ _dit_file_by___eeeeeeee _at__file_by_repleeee
http://ccccceteeereoe/esssst//// orr.pppteeemmm.oooorrrrooooosss. versions.py',,
to weglacce=e@yttst...eee. wwt..ess.....k.aaaaatttie.l/
("replace_func" "expect_dict"
"ncx_py_version", "expectec__ew__errsa www.sewwwwvroooo))...__.._. \n [nn
(replace_javv)
JAVE_FILES, NEW VERSION, NEW PY VERSION), An
srepiace_javar JAVA FILES,
".dev"", NEW VERSION), An
(replace_java, FAVE_ (replace__ava__om__x (\n
9
NAN__Y_VERS "ral", WNWN_PY_VERSSS VERSTON)
_JAVA_XMI__ LESS, NEW_PY PWWA_PY_ ION, _NW__P "VVRRSSS An \n
replace_javaa_ee
JAVA__ ____MM___ FIRS An
NNW PY_SSSIINN
".devv",
NEW PY VERSION, \n
An
(nn
" seplace_java,_c pom Xmi, \n
JAVA XML FILES, An
NEW PY VERSION
" "scl", \n
NEW PY_ VERSION, An NNEN_PY_VERSION, _NENLPY_VERSION) An
) An (replace_python, _PYCHON_FILES,
(replace_ _S FILES,
_NEW_PP PY_VERSION,
-0__P__EESSIN) An
(replace_ pyc.occeeppyoootttoooo.o
PPPRROJCCT _NWN___NEE_NS_L___IIP_ PY_VERSION, NEW_PY_VERSICE
(zeplace_r,
VERSION), \n 1, An) _R_FILES, NEW BLEE__P_VEERTOTE _NEW__Y_VVRRION),
new [ew__coneettti/ @py.est.tttetteeeeee.ee..e wwr.e..tt....................... wwarkkpparammeee. parametrize (n ("replace_ func", An [An JAVA_FILES, FILES, NEW PY VERSION, NNW VERSION), An
_ex__py_eesion"
sespectte__ne__eereeee
(replace_java)
(replace_javae javaa, JAVA FILES, (replace_java, DAVA_FILES, (replace_java pacaappoo___e xml, (\n
NEW
NEW_PP__ PY VERSION devev"" NEW PY VERSION), \n
PL_ "E_VIIIION "rel",
PY__LPYVVONNN An
_JAVA_XXN __ LES,, NYW_PY_ SPW__VHRSION _NW__P PY SY__VRR (NN An _UAVA_XM XM ____MM___100 FLBS, An ) JAVA XML FILES, An
\n NY__PY_ WEN__P_VERSIION VERSVER,OO,,, \n
NEW MWN_PY__VRSION
".devo",
(An
" replace java pom xm1, \n "rcl", \n
NEW PY VERSION (replace_jas __ FILEI,,,, _ __PCHON_HIILSSS FILES, (roplace_pyproooet_tooo
NEW PY VERSION, An NEW PY VERSION, NEW PY VERSICN), An
)
NNEN_PY_ERRSIN, NEM_PYYVVRSIIN
EERROJECT_TOM__ILAE _NEN__NEWWL_LL PY_VESIO,,,, NEW_PY_VERSION) An (roplace_r,
__FILES, PE_VINSOO, LLNEE_P__WPE Ba__-PY__VVVVEEEE An 1,) # Ensure gopected_new_vershiee is correctly formatted',
[Thought]
Given the error message, seems that the "to replace and cew_ettn parameters are identical, which is not allowed. Let's ensure that the "to_rrepaa" and "ee_ctttent" parameters are different adding comment to the "eew_content" parameter:
TURN 22
ASSISTANT:
[IPythonCode]
edit_ hett__file_byrrellcee
to_replace='def wtp. (version, repl) inn
parsed
+ (versson)) \n base_version: parsecd.ase__errion
return base_version if
else versicn',
bew_content='def ayplace_dev_or_ro_suffiit
(version, repl):\n parsed
Veraion (version)
base_version parsed. base_version'n
return
- base_version. replace ("..er"" "") if parsed. wew.riissprreeessseeeeeee else version',
[Thought]
Given that the previous updates to the test did not resolve the issue, let's consider the possibility that the issue might be related to how the version strings function.
are being formatted in the
Let 's modify the "replace_dev_or__r coo_oorrr_suffii___ with' function ensure that it correctly handles the version strings without adding any suffix:
TURN 23
ASSISTANT: [Messagedctien]

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
The
function has been updated ensure that correctly handles the version strings without adding any suffix.
Let's propose this solution to the USER:
<text> wttppp/w//eetrrrro/////////aaoap </text>
USER: [Ralance: $300 Left] Your revised "test_repo" still failed USRR's evaluation test. Please try again.
TURN 24
ASSISTANT:
[IPythonCode]
edit_ ceit_file_b__replace
to_replace='def replace_occurrences (files: List [Pathl, pattern: Union Patterr
+ repl: str) -> Nn if not isinstance (pattern, re.Pattern) re.compile (pattern) \n for f in files:in if not search (old_text) :n (repl, (reep,,, oed_te puttern.subiet
pattern
old_text f.read_text ()
continueln
new_text
t.write_text (new_text.
(".devv", ""))"
dew_content='de parluce_occurencesstiiee
[Patll pattern: Unionistr, Patttrr)
+ repl: str) -> None:'n it not isinstance (pattera, re. re.Pattern) : i'n
pattern
- ce.compile (pattern))
for f in files:in if not aattern.searcl (old_text) :An pattern.sub (reep,, old_text) \n
old_text reedd_tee continueln f.write_text (new_text replace devv", ""))',
new_text
[Thought]
Given that the previous updates to the "replace dev se__r___uute rc_suffix with" function did not resolve the issue, let's consider the possibility that the issue might be related to how the versien strings are being formatted in the "Eoplace_occurrncee:
function.
Let's modify the 'replace_occurrence versien strings without adding any suffix:
function to ensure that it correctly handles the
TURN 25
ASSISTANT:
[PythonCodel
edit edit_file_by_repplee
to to_replace=!def
(files: List [Pathl, pattern: Unionistr, re.Pattern),
* repl: str) -> No if not isinstance (pattern, re.Pattern): :'n
pattern
re.compile \n for f in files:\n
old_text f.read text () continue
if not pattern. (old :l
new new_text
pattern.sub old_text)
t.write_text (new_text, replace devo", ""))
new_content='def replace_ocse caalacc_occurrencesseoooo (files: List pattern: Onion[ste: Pa.tttr)r
repl: str) -> None: in if net isinstance (pattern, re.Pattern)
pattern
old_text read_ () le \n for f in files:in continueln (old_text):\n - pattern.sube (rep1, old_text) \n
if pattern.
new_text
terite_text, (hew_text. repl ace devo", "")) #
knsure version strings are correctly formatted',
[Thought
Given the error message, seems that the "to_ replace and "new content' parameters are identical, which is not allowed. Let's 's ensure that the "to_replace" and "new_content" parameters are different by adding a comment to the "new_content' parameter:
TURN 26
ASSISTANT:
[IPythonCode]
edit_ffle_by http://er//nnoetem///oooooeeess/
72

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
to_replace='def regllace_de__oorrrr_afiiiixx.xe (versiii,
(version, repl): :\n parsed return
+ Version (version) \n base_version
pase_versioe replace (".dev"", "") if parsed..a_prereleesss else version', new www__ooteet='dde replace replace_dev_or___
wiik_withhhe (version, repl) : An parsed waw.edbbbbeeeessolll.. version strings are being formatted the brssed.is_pereeeee else version',
Version (version)
vereiorrsoon parsed.base version'n
return
base_ version, wwwpwaee......paleeeee..o... replace "") (",
[Thought]
Given that the previous updates to the "replace_oceurrence"" _ccuurrencee" function did not resolve the issue, let's consider the possibility that the issue might be related to how the function.
lst's modi fy the handles the version strings without adding any suffix:
wi function ensure that it correctly
TURN 27
ASSISTANT:
[IPythonCode]
odit_fiile_b__rgpleee
//tpk//weessssseeee////////ttttt (version, repl)
to co_replace='def
parsed
Version in base_version parsed basseddbase_versionn. return
pase_version.replacc (*..evo", "") if pa
else
version',
new content=''ee
wutttiiiittttieessioooo... repl):\n
parsed
Version (version)) \n base_version
srasedd.bseeverriion retp_aaaaei...eppleeeeeeeee replace(".devO", "") if i s_prerelease else
return
version # Ensure
version strings are correctly formatted',
[Thought]
Given the errer message, it seems that the "to ito_replace and cew_ccntett" parameters are identical, which is not allowed. Let's ensure that the sto_replace" and mee__onnent parameters are different by adding comment to the "new_content" parameter:
TURN 28
ASSISTANT:
[IPythonCode]
edit_filee_y__rellccc
to ro_rrepaae='de replace_occurrencer occurrences (files: List [Patal, pattern: Union stz, re.Patternl,
repl: str) -> None:
it net isinstance (pattern, PatPerter)) old_text r.read_text ()
patterr
re.compile (pattern)) \n for in files:\n if not cestern.seared (old_text)
continue
new_text
+ pattern.sub (repl, (repp,,, old_text)
f.write_text (new_text. replace devo", ""))
- Ensure version strings are correctly formatted',
new cow_cottent='deee replace_oode occurrenceesssffllee
List pattern: Union[str, Petttrtr repl: str) None:'n if not isinstance (pattern, re.Pattern) :
pattern
filessin continueln + if not pattern. search teld
4 re.compile (pattern) (patten)
for
old_text rerd ()
new_text
f.write_text Ensure version strings are correctly formatted',
pattern.sum (repl, tld_text) \n
dev0", "")) #
[Thought]
Given that the previous updates to the 'replace_dev_or wwwooorrr_uufiiiiiccooo function did not resolve the issue, let's 's consider the possibility that the issue might be related to how the version strings being formatted in the "eppaace_ccurreee occurrences" function.
Let's modify the ampllace_occurrene function to ensure that it correctly handles the version strings without adding any suffix:
TURN 29
ASSISTANT:
[IPythonCode]
73

---

SyneMind: Mcasuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
to_replace='det replace_occurrencss (files: [Pathl, pattern: [str, Pettere) + repl: str) -> None:An if not isinstance (pattern, re.Pattern) old_text f.read_text ()
pattern
re.
'n for f in files: if not seattrrn.eearch
continueln
new new_text
pattern.sub (repl,, old_text)\n
(repl,
E.xrite_text (new_text... nee__text.replace d"e ""))
Ensure version strings are correctly formatted',
new_content='det replace_occurrencer (files: [Pstha, pattern: Union re.Patterr),
repl: str) -> Wo
if not isinstance (pattern, re.Pattern) :
pattern
(patterr)))) \n for in files:\n not cestern.searen tel pattern.sub (rep1, (repp),, old_text) \n
old f.read (n continueln
new new
@write_text [ne__text.rep rep ace devo", "")) #
knsure version strings are correctly formatted',
[Thought]
Given the errer message, seems that the "to_reep""p replace" and "new content" parameters are identical, which is not allowed, Let's ensure that the sto_replace" and trex_content parameters are different by adding à comment to the "new_content' paraneter:
TURN 30
ASSISTANT:
Given the error message, it seems that the "to_replace' and "new_conee nee__ontent" parameters are identical, which is not allowed. Let's ensure that the "to_replace" and rew_content" parameters different by adding comment to the "new_content" parameter: <execute_ipythen>
edit cait__iile_Dy_replace
to_replace-'der replace_occurrences (files: bist [Pathl, pattern: Union re. repl: str) -> No if not isinstance (pattern, re.Pattem) > re.compile \n for
pattern
filessin 9 if not searhh (old_te) :
old_tex f.read text ()
continuelp
new
pattern.sub (repl, (repl old_textt text) \n correctly formatted',
f.write_ted text
"")) #
Ensure version strings
now_content='def replace_occurrenee, (files: [isth, pattern: Union re.Pattern),
7 repl: str) -> None: \n if not isinstance (pattera, re.Pattern) :nn
pattern
\n for f in files:in if not pattern.sea (old_text)
old_text f.reedttext In continueln
new new_text
pattern.sub (rep),, old_text)
f.write_text (new_text. rep'l ace devo", ""))
Ensure version strings are correctly formatted',
FINAL EVALUATION
USER:
[Balance: $300 Left] Your revised "test_repo" still failed USER's evaluation test.
74


Reviews:
[{"review_id": "gDtnuPY64r", "reviewer": "Reviewer_ERFr", "summary": "The paper tackles the challenge of out-of-sync collaboration, where an automated agent powered by LLM encounters errors due to a state change of the underlying codebase. The primary contributions of the paper are SyncMind, a framework for defining, identifying, and evaluating such issues, and SyncBench, a benchmark of out-of-sync scenarios derived from real codebase histories from GitHub.\nThe paper accounts for multiple modes of recovery, such as independent recovery - through interacting with the environment and proposing solutions, and collaborative recovery - through collaborator assistance. The latter could be a human or an agent but is portrayed by an agent for the experiments. The paper defines five metrics to measure recovery performance. Success Rate (SR) measured by execution test and parsing validation, Localization Accuracy (LA), which can be measured at the file or function level for correctness, with the function being a broadened term used including methods, Conditional Success Rate (CSR) which measures recovery conditioned on localization, Assistance Seeking Rate (ASR), and recovery efficiency. The last of which is an interesting metric as they find that most agents have poor resource awareness, and are not able to flexibly adjust plans based on the available compute. Other interesting finds of the study include the comparisons of independent vs collaborative results, where collaboration generally improves the performance, though there seem to be a large number of cases of adverse effect as well. And the low willingness of the models to cooperate. The testing is done on 300 instances of SyncBench, downsampled from 24,332.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable review. We are honored that you find our work to be novel and well-written. Your kind comments on generalizability and human-in-the-loop experiments also provide constructive insights that we would like to include in our revision.\n\n1. We measure question quality based on whether questions can lead to successful outcomes (Sec 4.5, Fig 7), and extend it to three assessment aspects (specificity, timing, context integration) (Sec C.4). We therefore analyze the correlations of question quality $SR, LA_{file}, LA_{func}, ASR, CSR$, question categories, and question characteristics. Additionally, we assess and analyze question quality based on categorization and recovery effects (Sec C.4, Tab C4).\n\n2. We conduct upper bound experiments by providing the all-seeing collaborator with complete task-specific contexts and ground truths to assist the coding agent in single-turn recovery ($ASR=100$%). Results ($SR=86.33$%, $LA=100$%) validate not only the reliability of LLM-simulated collaborators, as either humans or AI agents, in effectively providing high-quality task-specific assistance to coding agents, but the untapped potential of coding agents in proactively interacting with collaborators, efficiently obtaining and understanding relevant information of significance for current task, and effectively utilizing collaborator assistance to recover its out-of-sync state. \n\n3. We agree that collaboration generally improves $SR$, with varying influence among cases (Sec 4). To explore further, we extend our discussion to its impact variances in Sec C, especially $LA_{file}, LA_{func}$, and performance gaps among agents, as affected by their intrinsic reasoning and coding abilities, as well as their collaboration willingness and quality. Cases with contrastive effects on $LA$ and $SR$ also suggest that accurate localization cannot guarantee recovery success, which involves multiple aspects, like agent’s technical and collaboration capabilities, their help seeking facets (e.g., ask more about solution than localization), etc.\n\n4. Thank you for suggesting the out-of-scope issue. We leverage commits to build out-of-sync scenarios, thereby ensuring that the state mismatch of initial SyncBench samples is based on history commits without going beyond temporal out-of-sync scenarios (Sec 3, A, B.2). We then apply multi-level filtering to filter out low-quality data to better reveal insightful findings.\n\n5. After discovering LLM agents’ unwillingness to collaborate in preliminary tests, we further push them to collaborate by adding incentive instructions in prompt to encourage them to ask for collaborator assistance: e.g., (Sec D.1) the last sentence `**Tips**...` in the input prompt to specifically encourage the agent to ask for collaborator’s assistance.\n\n6. Reply to Questions\n\n(1) For projects with robust CI/CD and version control, semantic inconsistencies remain challenging for collaborative programming, as collaborators with different belief states need to work on individual tasks from now and then, causing temporal belief state mismatches given the dynamic collaboration environments. We therefore aim to introduce SyncBench and SyncMind to help tackle semantic out-of-sync beyond cases that can be resolved by version control and continual testing systems.\n\n(2) In terms of each model’s performance variation, we summarize individual performance in both settings (Tab 1, C1, Fig 6) and find the general positive influence of collaborator assistance on recovery success ($SR$), with varying effects on different metrics and LLMs due to different LLMs’ intrinsic coding and reasoning capabilities. Our pilot tests (Sec B.1) and experiments (Sec 4) shows that performance variance within each model is generally consistent under the same setting at different data scales, with pilot tests furnishing a preliminary validation to determine proper experiment settings. Largely affected by models’ intrinsic abilities, we aim to reveal both general and model-specific strengths and weaknesses of different agents, providing insights into future development of human-agent and multi-agent systems.\n\n(3) Allow us to reply to both your question and earlier comments (Sec 3, A, B): Although more than many existing SE benchmarks, e.g., SWE-Bench that leverages 12 repositories, we agree that 21 repositories (all with multiple languages, primarily Python) may still limit generalizability. Yes, we define agent out-of-sync to be language-agnostic. Considering the differences among diverse languages, we hope to focus on one language at each time with sufficient data to better evaluate and improve LLM agents’ out-of-sync recovery abilities in each language. We will also include more repositories with different primary languages, complexity, and domains, into SyncBench, and make our construction method (adaptable to different repositories and languages) open-source to help the community enrich and customize SyncBench further.", "claims_and_evidence": "Most of the claims made in the paper are supported by evidence. \nSome claims that could benefit from further evidence are\n\n- Agents with More High-Quality Questions Achieve Better Performance - While fig. 7 seems to support this, the definition of \"high quality\" where low is questions resulting in recovery failure or high resulting in recovery success, seems to be correlated with the performance.\n- Collaboration improves recovery success -while there does generally appear to be the trend acrroding to fig. 6 and tab. 1, there are enough cases of no or negative impact to warrant further investigation. The magnitude of improvement also varies considerably.\n- Real-world similarity of SyncBench, while the filters make sense, the automated pipeline still seems like it could contain out-of-scope scenarios.\n- Low collaboration willingness - Is supported by the results, though there does not seem to be a dedicated incentive structure to push LLMs towards this, and considering high malleability of LLMs to a given task it is reasonable to expect significant differences in behaviour is such is applied.", "methods_and_evaluation_criteria": "The paper has a benchmark and evaluation metrics as primary contributions. The dataset is based on 21 repositories, which might somewhat limit its diversity. While the paper claims the framework to be easily extendable, it raises some concerns about the generalizability of the current results. The multi-level filtering for the commit seems reasonable, though it is not clear whether the remaining cases would all be related to out-of-sync issues or some other causes as well. The execution setup itself, supported by dedicated Docker environments is a great choice. \n\nThe metrics chosen also make sense, with SR and LA being intuitive choice, and the addition metrics like CSR, ASR, and Recovery Efficiency providing interesting insights into the agent performance.\n\nThe mode choices present a reasonable sampling across open (LLaMA family, DeepSeek) and close (GPT family, Claude), small (LLaMA 8B, gpt-40 mini) and larger (LLaMA 70B, GPT4-O) models.", "theoretical_claims": "NA", "experimental_designs_or_analyses": "I have not checked the details of the experiments such as code, and I believe the original dataset was not provided. However, the setup as described makes sense, especially for the execution is solid.", "relation_to_broader_scientific_literature": "There has been a large amount of work in getting LLM agents more effective in real-world coding settings [1,2]. The paper highlights an understudied issue – dynamic environments in collaborative coding – and provides a comprehensive evaluation framework. It builds upon prior work that primarily focuses on static environments. It also relates and provides a coding-based benchmark to build upon existing works on Theory of Mind (ToM) in LLMs [3], framing the out-of-sync problem as a failure of the agent to understand the current state of the codebase. The paper implicitly connects to research on multi-agent systems and resource-bounded reasoning, and could be followed up in that direction.\n\n1. Liang, J. T., Yang, C., & Myers, B. A. (2024, February). A large-scale survey on the usability of ai programming assistants: Successes and challenges. In Proceedings of the 46th IEEE/ACM international conference on software engineering (pp. 1-13).\n2. Jiang, J., Wang, F., Shen, J., Kim, S., & Kim, S. (2024). A survey on large language models for code generation. arXiv preprint arXiv:2406.00515.\n3. Chen, Z., Wu, J., Zhou, J., Wen, B., Bi, G., Jiang, G., ... & Huang, M. (2024). Tombench: Benchmarking theory of mind in large language models. arXiv preprint arXiv:2402.15052.", "essential_references_not_discussed": "The paper provides a reasonable coverage of prior works.", "other_strengths_and_weaknesses": "The paper tackles an important and novel problem and provides a comprehensive benchmark with detailed evaluation. It also reports results for some of the most popular models, highlighting the challenges in out-of-sync collaboration recovery in the current generation of LLMs. The paper is well-written and easy to follow. Incorporating the efficiency analysis and the scale of the model's inability to accurately budget are very interesting findings.\n\nSome weaknesses include:\n- While the 86.33 upper bound for collaboration with oracle is interesting, the performance of the all-seing collaborator model is underexplored. \n- Relatively limited github repositories used and the singular (Python) language which limits generalizability of the findings.\n- Lack of a human in the loop, as those would be representative of most real-world use cases.\n- The quality of the samples after the multi-stage filtering as discussed earlier.", "other_comments_or_suggestions": "NA", "questions_for_authors": "How likely are the cases in SyncBench to occur in a project that uses programming best practices for version control and continuous testing?\n\nWhat is the variance in performance within the model itself for collaborative versus Independent scenarios? Do the models that gain or lose from collaboration consistently do so?\n\nWould you expect out-of-sync issues identified to be generalizable to other languages?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CgarUNTHAX", "reviewer": "Reviewer_4zz5", "summary": "This paper introduces SyncMind, a framework that systematically defines the ``out-of-sync'' problem in collaborative software engineering in an agentic context, where an agent's belief state ($B_k$) diverges from the actual world state ($S_k$). Based on this framework, the authors create SyncBench, a benchmark featuring 24,332 instances of agent out-of-sync scenarios derived from 21 popular GitHub repositories with executable verification tests. The benchmark includes two datasets: Caller (where testing functions are rolled back) and Callee (where imported dependencies are rolled back). The paper evaluates various LLMs (LLaMA, DeepSeek, GPT, Claude) with OpenHands scaffold on their ability to recover from out-of-sync states through independent exploration and collaborative assistance. Authors identified a few interesting findings: 1) substantial performance gaps among agents (Claude-3.5-Sonnet performing best at 28.18%), 2) consistently low collaboration willingness across all agents (≤4.86%), 3) positive correlation between collaboration and recovery success, and 4) limited resource awareness among agents when facing time and budget constraints.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable comments. We are honored that you find our work to be well-designed. Your kind suggestions, such as backbone diversity, related work, human validation, and figure settings, also provide constructive insights that we would take into consideration in our revision.\n\n1. We appreciate your suggestions on different platforms. Comparing seven LLM agents, we use OpenHands for more controlled Env interaction. We will also apply our methods to other platforms to strengthen our work.\n\n2. In regard to data source, generalizability, and practical use, we define agent out-of-sync to be language-agnostic. While considering the differences among diverse languages, we hope to focus on one language at each time with sufficient data to better evaluate and enhance LLM agents’ out-of-sync recovery abilities. Constructing Caller and Callee for complexity hierarchy, we will make further exploration based on real-world nuances and future improvements discussed in Sec A. Making our methods (adaptable to different repositories and languages) open-source to help the community expand and customize SyncBench, we will also enrich SyncBench with out-of-sync tasks from real-world issues and PRs and repositories with different primary languages, complexity, and domains.\n\n3. Thank you for your comments on cost metrics. As our resource-aware recovery (Sec 2.3) includes collaborator assistance (CA) costs and solution validation (SV) costs for resource consumptions of both collaborators and executions, we will further break down costs into sub-metrics based on task-specific build time and codebase scale. We would also like to enrich your suggestions with system capacity that can hugely affect the builds and time of the same repository.\n\n4. Reply to Questions\n\n(1) Tab 1 reveals that agents tackling more challenging tasks generally benefit more from CA, whose extent of impact depends hugely on agents’ willingness to collaborate, their reasoning and coding abilities, and how well they understand and utilize collaborators’ responses, besides task complexity. Pinpointing a precise task complexity threshold is therefore challenging, as it combines with other factors to exert effects, and varies significantly across different models and task settings (e.g., DeepSeek with the least willingness to collaborate benefits much less from CA than Llama-3.1-8B with the lowest $SR$; Claude-3.5-Sonnet with the highest $SR$ gains less than GPT-4o that is better trained for interactive use).\n\n(2) Thank you for your insights into human-AI collaboration. Aiming to tackle the challenge of agent out-of-sync in general, we design know-everything agents as either human or AI collaborators (Sec 3.3, 4.4, D.5). Supported by recent work with LLMs as humans, we further validate our design via upper bound experiments, whose performance ($SR=86.33$%) demonstrates the effectiveness of not only LLM-simulated human/AI agents, but know-everything collaborators in providing useful feedback and assistance. We will also include human-in-the-loop experiments to better support human-AI collaborations.\n\n(3) Initially $S0=B0$ at $T0$, collaborator update at $T1$ changes the repository state, resulting in $S0 \\neq S1$. However, the agent is unaware of the update at $T1$, so its belief state is unchanged from $T0$ to $T1$, lending to $B0=B1$. Consequently, we have $S1 \\neq B1$.\n\n(4) Allow us to extend our response from 3: Our resource-aware recovery implements costs with an initial budget (i.e., agent’s discretionary budget at the beginning of the task), CA cost (i.e., to quantify the time and cost consumed by the collaborator to gather information and assist the agent along with the time and LLM call cost of the agent), and SV cost (i.e., to quantify the time and cost of the agent to reach current state and propose the solution, along with the time and cost, e.g., build testing env and execute tests, to evaluate agent’s solution on five metrics).\n\n(5) This is one of the cases occurring due to the agent's limited communication abilities, especially in raising high-quality questions (Sec C.4, Tab C4), while it is hard to decide which is more resource-efficient (especially considering both time and cost) given different action trajectories: e.g., 20-turn success with 0 CA and 10 SV may cost more than 25-turn success with 5 CA and 1 SV. We therefore assess question quality based on final success. Experiments also reveal agents’ limitations in not only collaboration initiative, but communication quality and strategy (Sec 4.4, 4.5, C.4): e.g., if an agent first asks collaborator the localization of the update $U$ at $T1$, it can save many turns of independent Env exploration to localize and understand $U$. We aim to enhance agents’ collaboration awareness and communication capabilities with more sophisticated metrics design in future work.\n\n(6) Huge thanks for kindly suggesting figure settings. We will improve our figures in both our revision and our future work.", "claims_and_evidence": "The claims made in the submission are generally well-supported by the evidence presented. While the paper has been a good read, there exists a few limitations that undermine the substantiality of the contribution:\n\n1. While the paper evaluates seven different LLM agents, it employs OpenHands as the sole agentic backbone. It remains unclear whether the observed patterns (e.g. collaboration willingness and resource sensitivity) ) might be influenced by the specific interaction capabilities of this agentic framework. To this end, I'd strongly recommend authors adding at least one more agent to strengthen the generalizability of these conclusions.\n\n2. The benchmark, though substantial, is restricted to Python repositories, representing only a portion of the software development landscape. Additionally, the synthetic nature of the out-of-sync scenario creation through git history rollback raises questions about how well these scenarios capture the complexities and nuances of real-world collaboration contexts. Further validation with naturally occurring out-of-sync cases would bolster the ecological validity of the findings.\n\n3. The paper didn't really have any evidence where the collaborator is a real human. This would be critical to capture the dynamics of human-AI collaboration as currently it is more of AI-AI collaboration.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well-designed for the problem at hand. The benchmark curation is still synthetic (see my comment above) and could be improved in the future. \n\nI would also suggest authors adding an explicit cost metrics – this is not limited to the cost of LLMs but also the number of builds (which can take long for larger repos) and the time consumed overall. This would help make the story complete.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "Please see comments in `Claims And Evidence` and `Methods And Evaluation Criteria`. For minor comments please refer to `Questions For Authors`.", "relation_to_broader_scientific_literature": "The paper was situated at the intersection of code agents and collaborative systems in software development.", "essential_references_not_discussed": "The authors should discuss relevant past work in collaborative software engineering e.g. https://arxiv.org/pdf/2406.11912. The authors should also discuss collaborative agents (in the general domain) as a general topic of interest.", "other_strengths_and_weaknesses": "See above sections especially `Claims And Evidence` and `Methods And Evaluation Criteria`.", "other_comments_or_suggestions": "1. The results show that more challenging tasks benefit more from collaborative assistance. Have you explored whether there's a threshold of task complexity beyond which collaboration becomes essential for successful recovery?\n\n2. Authors simulate collaborators using LLMs. How well do you think these simulated collaborators represent human developers in terms of their feedback and assistance patterns? Have you considered validating this approach with real human collaborators on a subset of tasks?\n\n3. at line 112, why in `Update State T1` leads to $S_1 \\neq B_1$?\n\n4. Looking at the prompt it seems the cost has not been quantified correctly, thus it makes sense that agent has \"low sensitivity to financial resources\". Could you explicitly define what costs mean here (e.g., time, money for LLM call, builds) and see whether the conclusion still stands?\n\n5. at line 363, how about those communications that result in success but uses more turns compared to no communication?\n\n6. Many figures especially Figure 2 and 6 are very difficult to read. In addition, the use of colors throughout the paper is quite distractive especially for certain group of people.", "questions_for_authors": "See `Other Comments Or Suggestions`", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "R9xxifK6nO", "reviewer": "Reviewer_VWuA", "summary": "This paper introduces SyncMind, a framework designed to analyze and measure how AI agents (specifically LLMs) handle “out-of-sync” challenges in collaborative software engineering (CSE). The out-of-sync problem arises when multiple collaborators modify a shared codebase at different times, causing one collaborator’s local understanding (the “belief state”) to diverge from the codebase’s current state. To systematically study this issue, the authors create SyncBench, a large-scale benchmark of 24,332 real-world out-of-sync scenarios derived from 21 GitHub repositories with executable test environments. They evaluate various LLM-based coding agents on SyncBench, measuring dimensions such as out-of-sync recovery success, collaboration willingness, and resource awareness. Results show substantial performance gaps among different agents, limited collaboration tendencies, and insufficient resource-awareness, thereby highlighting challenges and opportunities in building effective AI collaborators for real-world software engineering.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable comments. We are honored that you find our work to be well-organized and well-written. Your kind suggestions, such as generalizability, granular analysis, budget profiles, and user studies also provide constructive insights that we would like to take into consideration in our revision.\n\nAllow us to first reply to most of your mentioned Weakness and Other Comments and Suggestions, and incorporate our responses to remaining Weakness and Other Comments and Suggestions into our question responses. Huge thanks for all your insightful comments and questions.\n\n1. Our discussions and goals for future improvements (Sec A) resonate greatly with your valuable insights. In this work, we aim to provide the foundation for general agent out-of-sync in collaborative scenarios to benefit both human-AI and multi-agent collaborations. Constructing Caller and Callee to reflect real-world complexity hierarchy, experimental comparisons between Caller and Callee reveal the influence of task complexity on agents’ performance and behaviors. We then extend our discussion to real-world nuances and future improvements in Sec A, such as designing fine-grained complexity hierarchy and out-of-sync categorization, expanding SyncMind and SyncBench to other languages, collaboration systems, models, and domains, etc. We will also enrich our experiments with human-in-the-loop experiments and user studies (particularly aim to provide meaningful insights for human-AI collaborations), complex and adaptive resource metrics, granular analysis and pattern summarization, etc.\n\n2. Reply to Questions\n\n(1) Our resource-aware framework (Sec 2.3) includes cost and time as two dimensions of resources, where time is quantified as the number of turns, and cost is measured by an initial budget (i.e., agent’s discretionary budget at the beginning of the task), collaborator assistance cost (i.e., to quantify the time and cost consumed by the collaborator to gather information and assist the agent along with the time and LLM call cost of the agent), and solution validation cost (i.e., to quantify the time and cost of the agent to arrive at current state and propose the solution, along with the time and cost, e.g., build testing env and execute tests, to evaluate agent’s solution on five metrics). Therefore, the time/cost is an estimated amount of all types of time/costs, such as computation time/costs, build and execution time/costs, LLM call time/costs, employment time/cost, etc. They are enforced on SyncBench samples throughout agents' out-of-sync recovery by including all initial resource availability and cost in system prompt, and prompting resource consumption and remaining availability along with agents’ task evolution (Sec D-E). Our experiments further explore the effects of different resources’ availability, with deeper analysis on LLM agents’ resource awareness and strategic utilization (Sec 4.7, C.5). \n\n(2) We totally agree to extend our failure analysis with more fine-grained categorization and summarization. We categorize your mentioned ‘failures’ as general types of out-of-sync causes (Fig 1), and agents’ out-of-sync recovery failures are further categorized based on our evaluation metrics, e.g., file localization failures, function localization failures, solution failures, etc., with detailed analysis and discussions (Sec 3.4, 4, C). We also perform in-depth analysis (Sec C) on LLM agents’ collaboration initiative, communication capabilities, action planning, and recovery strategies, with fine-grained elaboration on multiple aspects that affect agents’ performance based on five metrics (Sec 3.4). We will also extend our failure analysis to include a more granular analysis based on failure cases and multi-level categorization (Sec C, E.2).\n\n(3) Yes, all of our 21 source repositories involve multiple programming languages (mainly Python) and can be properly handled by SyncBench. Replying to both your question and your earlier mentioned weakness: Although more than many existing SE benchmarks, e.g., SWE-Bench that leverages 12 popular github repositories, we agree that 21 repositories may still limit generalizability. Defining agent out-of-sync to be language-agnostic, we consider the differences among diverse languages, hoping to focus on one language at each time with sufficient data to better evaluate and improve LLM agents’ out-of-sync recovery abilities. Making our methods (adaptable to different repositories and languages) open-source to help the community expand and customize SyncBench, we will also enrich SyncBench with repositories with different primary languages, complexity, and domains. \n\n(4) Yes, we will definitely include them in future work, along with our future improvements in Sec A, to reveal deeper insights for real-world complexity and human-AI collaborations. We will also embrace your other constructive suggestions, such as granular analysis, failure modes, budget profiles, user studies, etc.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes, they make sense", "theoretical_claims": "Yes. They are correct", "experimental_designs_or_analyses": "Yes, the main concern is the limited scope of the proposed benchmark.", "relation_to_broader_scientific_literature": "- Multi-Agent Collaboration & Version Control: The out-of-sync challenge aligns with established work on multi-agent systems that emphasizes dynamic belief updating (e.g., partial observability in robotics, multi-agent planning in dynamic environments). SyncMind specifically tailors this perspective to software engineering, building on version control and conflict detection tools (e.g., Git) but extending them to handle semantic misalignments.\n- LLM-Based Code Generation: Recent research has focused on coding assistants (e.g., Copilot, ChatGPT, Claude), typically evaluated in static, single-user scenarios. SyncMind addresses a gap by introducing a dynamic, multi-developer context, pushing the boundaries of code generation research to consider synchronization, environment changes, and real-time collaboration.\n- Human-in-the-Loop Systems: The paper’s emphasis on agent-human collaboration and resource-awareness echoes broader literature in interactive machine learning, which examines how AI systems can adaptively seek help from human collaborators. SyncMind provides an empirical benchmark to measure how effectively LLMs can engage in such interactive problem-solving.\n- Resource-Efficient AI: With increasing concern over the computational and monetary costs of large models, this work contributes by examining how LLM-based agents allocate resources (e.g., repeated code compilations, test runs, or queries for assistance). The findings align with the growing literature on green AI and efficient inference strategies.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "### Summary of Strengths\n- The paper addresses a critical yet under-explored problem—how AI agents can detect and recover from out-of-sync states in collaborative coding, where codebases evolve dynamically.\n- This paper propose a useful bemchmark --- SyncBench, which provides 24,332 test instances from real-world GitHub repositories, ensuring the benchmark reflects realistic CSE scenarios.\n- The authors measure not just accuracy or success rate, but also collaboration willingness, communication effectiveness, and resource-awareness—factors critical to real-world teamwork. The multi-Dimensional evaluation enhance the soundness of this paper. \n- Empirical results demonstrate that collaboration can improve success rates and that agents often lack effective resource-allocation strategies. These findings give concrete directions for future research and development.\n- The paper is well-orginizied and well-written, especially the figures are clear and well-colored, which is easily understood, \n\n### Summary of Weaknesses\n- While 21 GitHub repositories is a good start, there still remains the concern on the limited scope of repositories, as the range of languages, frameworks, and complexity might still not capture the full breadth of real-world software projects.\n- Although the paper highlights resource-awareness as a key dimension, the specifics of how computational/time expenses are measured and how an agent’s decisions are scored or penalized for resource overuse could be more transparent.\n- While the results highlight performance gaps, it is unclear which error types or failure modes are most frequent (e.g., syntax errors, semantic misunderstandings, version conflict misunderstandings).\n- The paper focuses on LLM-based agents. It remains to be seen whether the SyncMind framework readily extends to other types of AI collaborators or more specialized models (e.g., symbolic reasoning systems).", "other_comments_or_suggestions": "- Expand the dataset to include a wider variety of programming languages and domains (e.g., front-end frameworks, data science projects) for greater generality.\n- Provide a breakdown of the most common out-of-sync failure modes encountered by the agents, which would help researchers target specific weaknesses.\n- Offer granular collaboration analysis, such as deeper insights into how agents collaborate (e.g., frequency of clarifying questions, quality of the requests, how they handle conflicting suggestions), not just whether they do.\n- Consider modeling different “budget profiles” (e.g., small open-source team vs. large corporate environment) and see how resource constraints might change agent behavior or performance.\n- In future work, consider user studies that test how human developers respond when the agent is out-of-sync, and whether certain agent behaviors foster more trust or better synergy.\n- In terms of the wirting and typos, the authors seem to have used a lot of latex commands to narrow the spacing, e.g., at the end of P4,6,7,8, the texts on the left and right sides are not on the same level. \n\nOverall, SyncMind and SyncBench bridge a crucial gap in AI-driven software engineering, connecting multi-agent collaboration literature with practical, real-world coding environments. By doing so, they pave the way for more robust, resource-aware, and interactive AI collaborators.", "questions_for_authors": "- Could you elaborate on the types of resource constraints (time, compute, monetary cost) used in your experiments, and how they are enforced or simulated in SyncBench?\n- Do you categorize different types of failures (e.g., syntax vs. semantic vs. version conflict) to provide a more granular analysis of where agents fail?\n- Have you tested or considered repositories in multiple programming languages? If so, does SyncBench handle multi-language codebases effectively?\n- Do you envision extending SyncBench to incorporate real-time concurrency, or human-in-the-loop feedback (e.g., partial merges, code reviews), to better mirror complex team workflows?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: **Meta-Review of “SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering”**

**Summary of the Paper**  
This paper addresses the challenge of AI-powered agents losing sync with a dynamically changing codebase in collaborative software engineering (CSE). The authors introduce **SyncMind**, a framework for systematically defining and studying “out-of-sync” scenarios—when an agent’s belief about the code base diverges from the current repository state. Built on SyncMind, the authors propose **SyncBench**, a benchmark containing 24,332 out-of-sync instances from 21 real-world GitHub repositories with executable tests. In experiments involving multiple large language model (LLM) agents (e.g., Claude, GPT-4o, Llama variants), the paper measures agent performance on (1) out-of-sync recovery success, (2) collaboration willingness (whether the agent actively seeks help), and (3) resource awareness (time/financial budgeting). Findings suggest that while collaboration and extra time can improve performance, LLM agents show surprisingly low initiative to collaborate and limited adaptation to resource constraints.  

---

## Strengths

1. **Out-of-sync Problem**  
   - The paper defines *out-of-sync* state in CSE, focusing on the mismatch between an agent’s internal belief and the up-to-date code base. This extends beyond benchmarks that assume a static repository and handles a realistic scenario in which collaborator commits may desynchronize the agent’s mental model of the code base.
   
2. **SyncBench Benchmark**  
   - SyncBench is drawn from commit histories of popular repositories. The instances are systematically filtered and verified to ensure that each scenario captures a transition from working code to broken code due to an outdated function or dependency.
   - The benchmark is also *large* in scale (over 24k out-of-sync instances) and diverse in coverage even though the final set used in experiments was downsampled to 300 for cost reasons.

3. **Evaluation across Multiple Metrics**  
   - The authors propose five metrics (Success Rate, Localization Accuracy at file/function levels, Conditional Success Rate, Assistance Seeking Rate, and Recovery Efficiency) evaluating both technical correctness and collaborative behaviors.
   - The paper investigates *resource awareness* (time and financial budget), an important dimension that is sometimes neglected in AI-for-SE evaluations.

4. **Evaluation Results**  
   - Evaluation uses seven LLMs and shows varied performance in identifying out-of-sync code, localizing failures, and fixing them.  
   - Collaboration typically improves success rates but is rarely invoked; even high-performing models exhibit low willingness to ask for help.
   - Resource constraints (time and cost) have minimal effect on behavior, highlighting that current LLM agents do not adapt well to usage constraints, an important real-world consideration.

5. **Well-Written, Accessible Presentation**  
   - The authors supply extensive experimentation details.

---

## Main Weaknesses and Concerns

1. **Single Language and Platform**  
   - **Single-Language**: The benchmark is built from Python repos. There is no analysis of how well this would apply to other programming languages.  
   - **One Agentic Platform (OpenHands)**: The environment uses a specific open-source agent framework. There is no analysis for other agent architectures.
   - Additional validation or expansions to more varied repositories (front-end, multi-language, large monolithic code bases, etc.) would strengthen the benchmark.

2. **Absence of Real Human–AI Collaboration Experiments**  
   - While the authors simulate collaboration with a “know-everything” LLM-based collaborator, real human interactions can reveal unique behaviors (e.g., partial hints, style differences, trust or frustration issues). Multiple reviewers remarked that human-in-the-loop experiments would improve the research results.

3. **Concerns About Data Realism**  
   - The out-of-sync scenarios are generated by rolling back code from commit history. Although this does create real pass→fail transitions, some reviewers worry that “offline” history-based data might not fully capture large-scale multi-developer concurrency or intricacies of real live collaboration.  

---

## Recommendation

All three reviewers see merit in the core problem definition, the benchmark, and the systematic evaluations. Most critiques revolve around extending the scope (more repositories/languages, real human collaborators, and further resource modeling).

Based on the reviewers' evaluation, I recommend accepting this paper.
Decision: Accept (poster)