Reviewerly/exHarmony huggingface dataset:
The NLBSE'23 Tool Competition

We report on the organization and results of the second edition of the tool competition from the International Workshop on Natural Language-based Software Engineering (NLBSE'23). As in the prior edition, we organized the competition on automated issue report classification, with a larger dataset. This year, we featured an extra competition on au-tomated code comment classification. In this tool competition edition, five teams submitted multiple classification models to automatically classify issue reports and code comments. The submitted models were fine-tuned and evaluated on a benchmark dataset of 1.4 million issue reports or 6.7 thousand code comments, respectively. The goal of the competition was to improve the classification performance of the baseline models that we provided. This paper reports details of the competition, including the rules, the teams and contestant models, and the ranking of models based on their average classification performance across issue report and code comment types.
HiNet: Novel Multi-Scenario &amp; Multi-Task Learning with Hierarchical Information Extraction

Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture. However, the MoE-based method, which aims to project all information in the same feature space, cannot effectively deal with the complex relationships inherent among various scenarios and tasks, resulting in unsatisfactory performance. To tackle the problem, we propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation, which achieves hierarchical extraction based on coarse-to-fine knowledge transfer scheme. The multiple extraction layers of the hierarchical network enable the model to enhance the capability of transferring valuable information across scenarios while preserving specific features of scenarios and tasks. Furthermore, a novel scenario-aware attentive network module is proposed to model correlations between scenarios explicitly. Comprehensive experiments conducted on real-world industrial datasets from Meituan Meishi platform demonstrate that HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions. HiNet is currently fully deployed in two scenarios and has achieved 2.87% and 1.75% order quantity gain respectively.
Stop Words for Processing Software Engineering Documents: Do they Matter?

Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. How-ever, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domain- specific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and identify 200 domain-specific stop words using traditional information-theoretic methods. Our re-sults show that the use of domain-specific stop words significantly improved the performance of research tools compared to the use of a general stop list and that 17 out of 19 evaluation measures showed better performance.
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation

Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preference in the time domain, omitting the fact that users’ behaviors are also influenced by various frequency patterns that are difficult to separate in the entangled chronological items. However, few attempts have been made to train SR in the frequency domain, and it is still unclear how to use the frequency components to learn an appropriate representation for the user. To solve this problem, we shift the viewpoint to the frequency domain and propose a novel Contrastive Enhanced SLIde Filter MixEr for Sequential Recommendation, named SLIME4Rec. Specifically, we design a frequency ramp structure to allow the learnable filter slide on the frequency spectrums across different layers to capture different frequency patterns. Moreover, a Dynamic Frequency Selection (DFS) and a Static Frequency Split (SFS) module are proposed to replace the self-attention module for effectively extracting frequency information in two ways. DFS is used to select helpful frequency components dynamically, and SFS is combined with the dynamic frequency selection module to provide a more fine-grained frequency division. Finally, contrastive learning is utilized to improve the quality of user embedding learned from the frequency domain. Extensive experiments conducted on five widely used benchmark datasets demonstrate our proposed model performs significantly better than the state-of-the-art approaches. Our code is available at https://github.com/sudaada/SLIME4Rec.
Amalur: Data Integration Meets Machine Learning

Machine learning (ML) training data is often scattered across disparate collections of datasets, called data silos.This fragmentation poses a major challenge for data-intensive ML applications: integrating and transforming data residing in different sources demand a lot of manual work and computational resources.With data privacy and security constraints, data often cannot leave the premises of data silos, hence model training should proceed in a decentralized manner.In this work, we present a vision of how to bridge the traditional data integration (DI) techniques with the requirements of modern machine learning.We explore the possibilities of utilizing metadata obtained from data integration processes for improving the effectiveness and efficiency of ML models.Towards this direction, we analyze two common use cases over data silos, feature augmentation and federated learning.Bringing data integration and machine learning together, we highlight new research opportunities from the aspects of systems, representations, factorized learning and federated learning.
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph

Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG-enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at https://github.com/OpenBGBenchmark/OpenBG.
Toward Efficient Automated Feature Engineering

Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation and selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show 2.9% higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods.
Reliable Transactions in Serverless-Edge Architecture

Modern edge applications demand novel solutions where edge applications do not have to rely on a single cloud provider (which cannot be in the vicinity of every edge device) or dedicated edge servers (which cannot scale as clouds) for processing compute-intensive tasks. A recent computing philosophy, Sky computing, proposes giving each user ability to select between available cloud providers.In this paper, we present our serverless-edge co-design, which extends the Sky computing vision. In our serverless-edge co-design, we expect edge devices to collaborate and spawn required number of serverless functions. This raises several key challenges: (1) how will this collaboration take place, (2) what if some edge devices are compromised, and (3) what if a selected cloud provider is malicious. Hence, we design ServerlessBFT, the first protocol to guarantee Byzantine fault-tolerant (Bft) transactional flow between edge devices and serverless functions. We present an exhaustive list of attacks and their solutions on our serverless-edge co-design. Further, we extensively benchmark our architecture on a variety of parameters.
An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases

Unit testing is a vital part of the software development process and involves developers writing code to verify or assert production code. Furthermore, to help comprehend the test case and troubleshoot issues, developers have the option to provide a message that explains the reason for the assertion failure. In this exploratory empirical study, we examine the characteristics of assertion messages contained in the test methods in 20 open-source Java systems. Our findings show that while developers rarely utilize the option of supplying a message, those who do, either compose it of only string literals, identifiers, or a combination of both types. Using standard English readability measuring techniques, we observe that a beginner's knowledge of English is required to understand messages containing only identifiers, while a 4th -grade education level is required to understand messages composed of string literals. We also discuss shortcomings with using such readability measuring techniques and common anti-patterns in assert message construction. We envision our results incorporated into code quality tools that appraise the understandability of assertion messages.
OpenEmbedding: A Distributed Parameter Server for Deep Learning Recommendation Models using Persistent Memory

In this paper, we present OpenEmbedding, a distributed parameter server system for deep learning recommendation models (DLRM) workloads. In order to support rapid growth in the number of features and the model size (Terabytes are common) of DLRM workloads, OpenEmbedding takes advantage of emerging persistent memory (PMem) to address scalability and reliability issues in training DLRMs. Compared to DRAM, PMem can have much lower per-GB cost, higher density, and non-volatility, while with slightly low access performance to DRAM. OpenEmbedding uses DRAM as cache and PMem as storage for the sparse features and develops a simple but effective pipeline processing approach to optimize the access latency of the sparse features in PMem. For reliability, we develop a lightweight synchronous checkpointing scheme that is specially co-designed with the pipelined cache to reduce the run-time overhead of checkpointing. Our evaluations on a real-world industry workload consisting of billions of parameters demonstrate 1) the effectiveness of our PMem-aware optimizations, 2) checkpointing mechanism with near-zero run-time overhead to the training performance and 3) fast recovery with up to 3.97× speedup compared to the state-of-the-art. OpenEmbedding has been deployed in hundreds of scenarios in industry within 4Paradigm, and is open-sourced <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
SketchConf: A Framework for Automatic Sketch Configuration

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github.
Partitioner Selection with EASE to Optimize Distributed Graph Processing

For distributed graph processing on massive graphs, a graph is partitioned into multiple equally-sized parts which are distributed among machines in a compute cluster. In the last decade, many partitioning algorithms have been developed which differ from each other with respect to the partitioning quality, the run-time of the partitioning and the type of graph for which they work best. The plethora of graph partitioning algorithms makes it a challenging task to select a partitioner for a given scenario. Different studies exist that provide qualitative insights into the characteristics of graph partitioning algorithms that support a selection. However, in order to enable automatic selection, a quantitative prediction of the partitioning quality, the partitioning run-time and the run-time of subsequent graph processing jobs is needed. In this paper, we propose a machine learning-based approach to provide such a quantitative prediction for different types of edge partitioning algorithms and graph processing workloads. We show that training based on generated graphs achieves high accuracy, which can be further improved when using real-world data. Based on the predictions, the automatic selection reduces the end-to-end run-time on average by 11.1% compared to a random selection, by 17.4% compared to selecting the partitioner that yields the lowest cut size, and by 29.1% compared to the worst strategy, respectively. Furthermore, in 35.7% of the cases, the best strategy was selected.
Instant Representation Learning for Recommendation over Large Dynamic Graphs

Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendation models start leveraging information from various behavior types exhibited by users. In real-world scenarios, the user behavioral graph is not only multiplex but also dynamic, i.e., the graph evolves rapidly over time, with various types of nodes and edges added or deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing methods neglect such streaming dynamics and thus need to be retrained once the graph has significantly evolved, making them unsuitable in the online learning environment. Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models. To this end, we propose SUPA, a novel graph neural network for dynamic multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture, SUPA develops a sample-update-propagate architecture to alleviate neighborhood disturbance. Specifically, for each new edge, SUPA samples an influenced subgraph, updates the representations of the two interactive nodes, and propagates the interaction information to the sampled subgraph. Furthermore, to train SUPA incrementally online, we propose InsLearn, an efficient workflow for single-pass training of large dynamic graphs. Extensive experimental results on six real-world datasets show that SUPA has a good generalization ability and is superior to sixteen state-of-the-art baseline methods. The source code is available at https://github.com/shatter15/SUPA.
Differential Aggregation against General Colluding Attackers

Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and analyze sensitive data while preserving users’ privacy. However, almost all LDP protocols rely on a semi-trust model where users are curious-but-honest, which rarely holds in real-world scenarios. Recent works [6], [11], [62] show poor estimation accuracy of many LDP protocols under malicious threat models. Although a few works have proposed some countermeasures to address these attacks, they all require prior knowledge of either the attacking pattern or the poison value distribution, which is impractical as they can be easily evaded by the attackers.In this paper, we adopt a general opportunistic-and-colluding threat model and propose a multi-group Differential Aggregation Protocol (DAP) to improve the accuracy of mean estimation under LDP. Different from all existing works that detect poison values on individual basis, DAP mitigates the overall impact of poison values on the estimated mean. It relies on a new probing mechanism EMF (i.e., Expectation-Maximization Filter) to estimate features of the attackers. In addition to EMF, DAP also consists of two EMF post-processing procedures (EMF* and CEMF*), and a group-wise mean aggregation scheme to optimize the final estimated mean to achieve the smallest variance. Extensive experimental results on both synthetic and real-world datasets demonstrate the superior performance of DAP over state-of-the-art solutions.
Learned Probing Cardinality Estimation for High-Dimensional Approximate NN Search

Approximate nearest neighbor (ANN) search in high-dimensional space plays an essential role in a variety of real-world applications. A well-known solution to ANN search, inverted file product quantization (IVFPQ) adopts inverted files to avoid exhaustive examination and compresses vectors using product quantization to reduce the space overhead. However, existing implementations use the same fixed probing cardinality (i.e., the number of cells to probe) setting for all queries, which leads to too many or too few cell examinations, thus increasing the average query latency or reducing the recall. To achieve a better trade-off between latency and accuracy, we enable probing cardinality estimation for high-dimensional ANN search by using deep learning techniques. We develop HBK-means, a hierarchical balanced clustering algorithm that reduces the data distribution imbalance of cells to enable a better estimation. Next, we develop PCE-Net, an encoder-decoder based neural network for estimating query-dependent minimum probing cardinality. In addition, we introduce two query optimization strategies: lower bound sorting based pruning (LBS-Pruning) and early termination (ET), to further reduce query latency. Extensive experiments with real-world data offer evidence that the proposed solution is capable of achieving better performance than IVFPQ and its variants.
Efficient Deep Ensemble Inference via Query Difficulty-dependent Task Scheduling

Deep ensemble learning has been widely adopted to boost accuracy through combing outputs from multiple deep models prepared for the same task. However, the extra computation and memory cost it entails could impose an unacceptably high deadline miss rate in latency-sensitive tasks. Conventional approaches, including ensemble selection, focus on accuracy while ignoring deadline constraints, and thus cannot smartly cope with bursty query traffic and queries with different hardness. This paper explores redundancy in deep ensemble model inference and presents Schemble, a query difficulty-dependent task scheduling framework. Schemble treats ensemble inference progress as multiple base model inference tasks and schedules tasks for queries based on their difficulty and queuing status. We evaluate Schemble on real-world datasets, considering intelligent Q&A system, video analysis and image retrieval as the running applications. Experimental results show that Schemble achieves a 5× lower deadline miss rate and improves the accuracy by 30.8% given deadline constraints.
HGMatch: A Match-by-Hyperedge Approach for Subgraph Matching on Hypergraphs

Hypergraphs are a generalisation of graphs in which a hyperedge can connect any number of vertices. It can describe n-ary relationships and high-order information among entities compared to conventional graphs. In this paper, we study the fundamental problem of subgraph matching on hypergraphs (i.e., subhypergraph matching). Existing methods directly extend sub-graph matching algorithms to the case of hypergraphs. However, this approach delays hyperedge verification and underutilises the high-order information in hypergraphs, which leads to large search space and high enumeration costs. Furthermore, with the growing size of hypergraphs, it is becoming hard to compute subhypergraph matching sequentially. Thus, we propose an efficient and parallel subhypergraph matching system, HGMatch, to handle subhypergraph matching in massive hypergraphs. We propose a novel match-by-hyperedge framework to utilise high-order information in hypergraphs and use set operations for efficient candidate generation. Moreover, we develop an optimised parallel execution engine in HGMatch based on the dataflow model, which features a task-based scheduler and fine-grained dynamic work stealing to achieve bounded memory execution and better load balancing. Experimental evaluation on 10 real-world datasets shows that HGMatch outperforms the extended version of the state-of-the-art subgraph matching algorithms (CFL, DAF, CECI, and RapidMatch) by orders of magnitude when using a single thread, and achieves almost linear scalability when the number of threads increases.