bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Accounting Professional Identity to Pave a Route Toward Sustainable Development with the Role of Inclusive Leadership
Authors: Pham Huy Quang , Vu Phuc Kien
Published: July 2024

This study intends to explore the interconnection between the professional identity of accountants (PIA) and sustainable development performance (SDP). It also aims to delve into the mediation mechanism of nonfinancial reporting (NFR) in the interconnection between PIA and SDP. Outstandingly, it makes many attempts to expand understanding of the moderating function of inclusive leadership (IL) in the proposed model’s postulated constructs. Statistical data from surveys given to the samples of accountants in small and medium-sized enterprises (SMEs) obtained from the two waves of data collection within the different periods, namely the period of lockdown and period of new normal, are used to empirically corroborate the hypothesized model, which is supported by structural equation modeling. The results analyses confirm, in terms of significance and effect size, the existence of positive relationships between the hypothesized dimensions in the suggested model. In addition to serving as a beneficial springboard for further research, the importance of more nuanced insights in this work can help practitioners and policy-makers sense and seize ways to reach sustainable development pathways in an efficient and effective manner.
Dynamic Modeling and Analysis of a Temperature- Dependent Axially Translating Functionally Graded Cantilever Beam
Authors: Zhao Bingxin , Gao Ruolin , Hu Rongchun , Deng Zichen , Gu Xudong
Published: July 2024

In this paper, the dynamics of an axially translating functionally graded cantilever beam (FGCB) with time-varying length are studied under environmental temperature variations. Firstly, the governing partial differential equation for the FGCB under different temperatures is established based on the Euler–Bernoulli beam theory. Secondly, the Galerkin discretization and the assumed mode method (AMM) are employed to derive the vibration equations for each mode. Then, the coupling effects of the axial translation motion and the bending deformation on the vibration response of the FGCB are analyzed through numerical simulation. The results showed that different initial lengths, velocities and accelerations can influence the dynamic response greatly. Moreover, the increase in temperature will increase the response amplitude and decrease the frequency of FGCB, but the increase in functionally gradient parameter will decrease both the amplitude and the frequency of the FGCB. Finally, the wavelet transform (WT) is utilized to perform a time–frequency analysis. It is found that the time-dependent frequency obtained by using WT is consistent with the first-order static frequency obtained theoretically. The variation of frequency with time can be obtained quickly and accurately by using WT. The results of this research are of great significance for the application of composite axially translating beams in practical engineering.
Prediction of the Upwarp Buckling Critical Temperature Rise for Longitudinally Connected Slab Track Under Joint Damage State
Authors: Xu Chang , He Jiajun , Liu Weixing , Xu Tianci , Zhao Pingrui
Published: July 2024

The joint of longitudinally connected slab track is vulnerable to damage from environmental impacts and repeated train loads. Under high temperature environments, the joint becomes a high risk area for track slab upwarp, which significantly affects the upwarp stability of the track slab and the safe operation of trains. This study employs the arc length approach and a finite element model to investigate the upwarp equilibrium development path of the track slab, the upwarp evolution process of the track slab under the joint damage is further analyzed. Then, the influence of different initial defects on the vertical stability of the track slab is studied. Utilizing dimensional analysis, a general formula for calculating the critical temperature rise of the track slab is proposed. The results are as follows: (1) The entire upwarp process of track slab under joint damage can be divided into three stages: stability, upwarping development, and instability. (2) A nonlinear correlation exists between the upwarp critical temperature rise of the track slab and three types of initial defects. Height defects in narrow joints exert the most significant impact on the critical temperature rise, followed by initial Out-of-straight (initial OOS), with the initial bending shape having the least effect. The complete defect of the narrow joint reduces the upwarp critical temperature rise of the track slab by 53.26°C, a 48.4% reduction. (3) A unified expression, derived from the characteristic parameters of initial defects and dimensional analysis, can more accurately predict the upwarp critical temperature rise of the track slab.
Seismic Performance of Steel Tube-Reinforced Concrete Columns after Exposure to Fire on Two Adjacent Sides
Authors: Bao Yanhong , Tang Chong , Yu Yang
Published: July 2024

A nonlinear finite element model has been developed to numerically simulate the hysteretic behavior of steel tube-reinforced concrete columns after exposure to fire on two adjacent faces under reciprocating loads. This model considers various parameters, including fire duration, slenderness ratio, section size, core area ratio, external concrete strength, and reinforcement ratio. The study systematically investigates and analyzes characteristics such as the skeleton curve, ductility coefficient, stiffness degradation, and hysteretic energy dissipation of columns post-fire. Results indicate that member stiffness decreases with increasing displacement loading, and the equivalent viscous damping coefficient of the member escalates with increased horizontal displacement. Moreover, as fire duration and slenderness ratio increase, there is a corresponding decrease in member stiffness, leading to a reduction in the equivalent viscous damping coefficient. In contrast, increases in section size and core area ratio enhance member stiffness and decrease the equivalent viscous damping coefficient. Furthermore, enhancements in external concrete strength and reinforcement ratio elevate member stiffness, which subsequently increases the equivalent viscous damping coefficient.
Numerical Analysis of Dynamic Stability of Flutter Panels in Supersonic Flow
Authors: Deng Jian , Liu Airong , Zhong Zilin
Published: July 2024

This paper introduces a novel numerical method for investigating the dynamic stability of a flutter panel exposed to a supersonic gas flow and a fluctuating axial excitation force. Initially, the system equation of motion was derived using Lagrange’s equation, where the first two modes are coupled Mathieu–Hill equations with damping, constituting a system of linear second-order differential equations with periodically variable coefficients. Subsequently, a new numerical method was proposed to analyze the dynamic stability of coupled Mathieu–Hill equations with damping. This method involves breaking down an arbitrary parametric load into discrete segments to approximate the variable excitation function using a step function. The system responses of each segment are then accumulated in matrix form. The proposed numerical method proves particularly effective for dynamic systems whose parameters cannot be treated as small. In practical application, the method allows the construction of instability regions corresponding to natural frequencies, subharmonics, and combination frequencies. Dynamic stability diagrams were generated based on dynamic pressure ratio, air/panel density ratio, Mach number, panel thickness—length ratio, and excitation frequency. The results demonstrated general agreement with those obtained through Hsu’s perturbation method, however, our numerical results have proven more accurate. The paper concludes by offering suggestions for suppressing panel flutter through appropriate parameter combinations.
Properties of generalized weighted core inverses in Banach *-algebras
Authors: Chen Huanyin , Sheibani Marjan
Published: July 2024

We present new properties of generalized weighted core inverse in a Banach *-algebra. We prove that the generalized weighted core inverse has the polar-like property. The reverse order law for generalized weighted core inverse is established. The properties of generalized weighted core orders are thereby investigated.
On the unit-Jacobson graph
Authors: Ulucak Gulsen , Isikay Sevcan
Published: July 2024

In this paper, we introduce the unit-Jacobson graph, which is defined by the unit elements and the elements of the Jacobson radical of a commutative ring [Formula: see text] with nonzero identity. We give relationships between this new graph concept and some special rings such as [Formula: see text]-clean rings, [Formula: see text]-rings, local rings, and cartesian rings. Moreover, we investigate the concepts of the dominating set, diameter, and girth on the unit-Jacobson graph.
Condensed Galerkin Time-Element for Structural Dynamics with Adaptive Time-Stepping in Maximum Norm
Authors: Yuan Quan , Yuan Si
Published: July 2024

In this paper, the newly-developed condensed Galerkin element of polynomial degree [Formula: see text] for first-order initial-value problems is reformulated for and extended to the structural dynamic equations, yielding a high-performance dynamic element of one-step, unconditionally stable type with the conventional finite element (FE) convergence [Formula: see text] within the element and the extra-superconvergence [Formula: see text] at the element end-nodes. Further, based on the element energy projection (EEP) technique, a superconvergence EEP formula for the condensed element is proposed in the paper and mathematical analysis is presented to prove that the derived EEP solution gains a superconvergence [Formula: see text], which is an order higher than the FE solution of [Formula: see text] for all element degree [Formula: see text] and is hence qualified to serve as a point-wise error estimator. As a result, a simple, efficient, and reliable algorithm with adaptive time-stepping controlled by the maximum norm is proposed. Representative numerical examples are presented to verify the validity of the proposed theorems and to demonstrate the high performance of the proposed element and the associated adaptivity algorithm.
Translation functors for locally analytic representations
Authors: Jena Akash , Lahiri Aranya , Strauch Matthias
Published: July 2024

Let G be a p-adic Lie group with reductive Lie algebra [Formula: see text]. In analogy to the translation functors introduced by Bernstein and Gelfand on the categories of [Formula: see text]-modules, we consider similarly defined functors on the category of modules over the locally analytic distribution algebra [Formula: see text] on which the center of [Formula: see text] acts locally finitely. These functors induce equivalences between certain subcategories of the latter category. Furthermore, these translation functors are naturally related to those on category [Formula: see text] via the functors from category [Formula: see text] to the category of coadmissible modules. We also investigate the effect of the translation functors on locally analytic representations [Formula: see text] associated by the p-adic Langlands correspondence for [Formula: see text] to two-dimensional Galois representations V.
Effect of Butylated Hydroxytoluene Nanoparticles Blended with Biodiesel Derived from Bauhinia Purpurea Linn Seed Fueled in a CRDI Diesel Engine
Authors: Lionus Leo G. M. , Munuswamy Dinesh Babu , Jayabal Ravikumar , Devarajan Yuvarajan
Published: July 2024

This study sought to investigate techniques for lowering diesel engine emissions by using waste seed as an alternative energy source in place of conventional fossil fuels. This study thoroughly investigated the process of transesterifying waste seed oil obtained from Bauhinia purpurea linn for use as fuel in a common rail direct injection (CRDI) diesel engine. Butylated hydroxytoluene (BHT) nanoparticles serve as a combustion enhancer. The engine outcome metrics were analyzed using diesel, Bauhinia purpurea linn biodiesel (BPLB) and BPLB-BHT blends ([Formula: see text]m BHT 10 ppm and [Formula: see text]m BHT 10 ppm) without changing the operating circumstances. At full brake power, the [Formula: see text]m BHT 10 ppm mix outperformed the BPLB and [Formula: see text]m BHT 10 ppm blends, but fell short of diesel. The [Formula: see text]m BHT 10 ppm blend reduced smoke opacity by 39.14%, hydrocarbon (HC) emissions by 42.71%, carbon monoxide (CO) emissions by 59.03% and oxides of nitrogen (NOx) emissions by 12.29% compared to neat diesel fuel at maximum brake power.
Viscous Dissipation, Inclined Magnetic Field and Joule Heating Impacts on Mixed Convection MHD Oscillatory Diffusion-Radiative Casson Fluid Flow with Chemical Reaction Over a Slanted Vertical Porous Plate
Authors: Prabhakar Reddy B. , Mng’ang’a Jumanne , Matao M. Paul
Published: July 2024

This work analyzed numerically the impacts of viscous dissipation, Joule heating and inclined magnetic field on reactive-diffusion magneto-hydrodynamic radiative mixed convection oscillatory non-Newtonian Casson fluid (CF) fluxing across a slanted semi-infinite vertical plate inserted in a porous medium. The framed dimensional flow controlling partial differential equations were modified to dimensionless partial differential equations by bringing in applicable scaling variables and then numerically solved by imposing the finite difference scheme. The outcomes are established with graphical representations to inspect the flow fields’ performance for diverse flow parameters. At the same time, numerical data of skin friction and heat and mass transferal rates near the surface area are presented in a tabular format. This research study discovered that the viscous dissipation and radiation effects intensify the temperature and velocity fields while heat ingestion has a contrary effect. Both velocity and concentration distributions are diminished by the chemical reaction and Schmidt number while the converse trend was noted with thermo-diffusion effect. The velocity distribution was narrowed by the angled magnetic field, Casson parameter, and magnetic field but the porosity parameter exposed the opposite impact. The influence of the magnetic field and Casson parameters incited to decline the friction. Heat absorption in the flow makes the Nusselt number rise but improving viscous dissipation and radiation effects have pointed to an opposite trend. The chemical reaction parameter increases the Sherwood number but thermo-diffusion decreases it. Further, validation with already published results is accomplished and an excellent agreement is realized.
The effect of sudden fluctuations on a treated Hepatitis C model
Authors: Bertoune Ayoub , El Fatini Mohamed , Nasri Bouchra R. , Sekkak Idriss
Published: July 2024

In this paper, we consider a Hepatitis C model characterized by acute and chronic infections, treatment as a health-protective measure for the chronically infected is proposed, and its effect on the population is further highlighted. The model is perturbed by white noise and we incorporate Lévy jumps as means to depict abrupt fluctuations. We demonstrate the solution’s existence and uniqueness and deduce adequate criteria for the disease’s extinction and persistence. The importance of treatment as a protective strategy is manifested in its ability to eradicate or mitigate the propagation of the disease. We present numerical simulations to demonstrate the theoretical findings we have obtained.
On treeings arising from HNN extensions
Authors: Kida Yoshikata
Published: July 2024

For certain HNN extensions including Baumslag–Solitar groups, a treeing is constructed from their certain probability-measure-preserving actions. This is a treeing of a quotient groupoid of the translation groupoid associated with their actions. As its application, for some of those HNN extensions, we show that the kernel of the modular homomorphism is measure equivalent to the direct product of the free group of infinite rank and [Formula: see text].
Acoustic Radiation Characteristics of a Forced Vibrating Elastic Panel Under Thermal Environments
Authors: Gascón-Pérez Manuel
Published: July 2024

This paper presents the thermo-acoustic frequency response of an un-baffled rectangular panel subjected to an external excitation load. A boundary element method BEM has been employed taking into account the Kirchhoff-Helmholtz K-H integral equation for the acoustic pressure and with the fluid-plate interface boundary condition the acoustic pressure jump over the panel is calculated. The thermal effects are considered regarding in the form of a uniform increment of temperature of the panel and are analysed in order to prevent the buckling phenomena. The excitation force considered is in the form of a concentrated load at some point of the panel and the deformation modes correspond to the vacuum case. Applying a collocation method for the panel equation, a frequency transfer function is obtained that relates the deflexion of the panel with the applied load. The effect of several geometric parameters, different thermal loads and location of the load applied on the acoustic radiated power and the acoustic efficiency spectrum are evaluated. Furthermore, the influence of the excitation frequency on the sound directivity is evaluated. The verification of the method is proven with other works.
Embarking on a Preliminary Exploration: Cross-Lingual Embedding in English-Manipuri
Authors: Naorem Deepen , Singh Sanasam Ranbir , Sarmah Priyankoo
Published: July 2024

This study explores cross-lingual embedding techniques between English and Manipuri, a Tibeto-Burman language characterized as low-resource. Initially, the focus lies on assessing cross-lingual embedding quality through evaluation of bilingual dictionary induction across various supervised, weakly supervised, and unsupervised methods. Results indicate suboptimal performance across all state-of-the-art techniques for English-Manipuri cross-lingual embedding. Subsequently, attention shifts to investigating the influence of linguistic factors, such as Manipuri’s morphologically rich nature and differing sentence structure, on Bilingual Dictionary Induction (BDI) task performance. Additionally, the study demonstrates that the sparsity of Manipuri data, relative to English, impacts the quality of cross-lingual embeddings.
SGCAL: An Algorithm to Identify Sensitive Gene Combinations in the Mouse Osteoblast Gene Network
Authors: Chen Nan , Chen Yaru , Hu Yongfei , Wang Haohua
Published: July 2024

Osteoblasts originate from mesenchymal stem cells (MSCs) and their maturation is regulated by numerous genes. However, specific genes that play decisive roles in osteoblast maturation are not completely understood. Herein, we propose a new algorithm called SGCAL to identify sensitive gene combinations during osteoblast development and decode the regulatory roles of sensitive gene combinations in cell development. In contrast to conventional gene identification algorithms, the SGCAL algorithm, which is based on a combination of ARNN-LNE iterative cycles, quantitatively evaluates the sensitivity of genes by analyzing the average network entropy before and after gene perturbation. We verified that osteoblast development is fundamentally regulated by an overall sensitive network formed by specific genes. Moreover, we explored the roles of these sensitive genes and their combinations in the transition time point of the initial state of network change. Our algorithm was validated on two independent mouse osteoblast development datasets, successfully identifying sensitive genes and their combinations in both datasets and revealing that gene combinations containing Col1a1 can exhibit significant sensitivity. This study demonstrated the universality of cell fate and network regulation, offering new insights and a theoretical basis for the treatment and repair of bone diseases
Characterizing Python Library Migrations
Authors: Islam Mohayeminul , Jha Ajay Kumar , Akhmetov Ildar , Nadi Sarah
Published: July 2024

Developers heavily rely on Application Programming Interfaces (APIs) from libraries to build their software. As software evolves, developers may need to replace the used libraries with alternate libraries, a process known as library migration. Doing this manually can be tedious, time-consuming, and prone to errors. Automated migration techniques can help alleviate some of this burden. However, designing effective automated migration techniques requires understanding the types of code changes required to transform client code that used the old library to the new library. This paper contributes an empirical study that provides a holistic view of Python library migrations, both in terms of the code changes required in a migration and the typical development effort involved. We manually label 3,096 migration-related code changes in 335 Python library migrations from 311 client repositories spanning 141 library pairs from 35 domains. Based on our labeled data, we derive a taxonomy for describing migration-related code changes, PyMigTax. Leveraging PyMigTax and our labeled data, we investigate various characteristics of Python library migrations, such as the types of program elements and properties of API mappings, the combinations of types of migration-related code changes in a migration, and the typical development effort required for a migration. Our findings highlight various potential shortcomings of current library migration tools. For example, we find that 40% of library pairs have API mappings that involve non-function program elements, while most library migration techniques typically assume that function calls from the source library will map into (one or more) function calls from the target library. As an approximation for the development effort involved, we find that, on average, a developer needs to learn about 4 APIs and 2 API mappings to perform a migration and change 8 lines of code. However, we also found cases of migrations that involve up to 43 unique APIs, 22 API mappings, and 758 lines of code, making them harder to manually implement. Overall, our contributions provide the necessary knowledge and foundations for developing automated Python library migration techniques. We make all data and scripts related to this study publicly available at https://doi.org/10.6084/m9.figshare.24216858.v2.
DyPyBench: A Benchmark of Executable Python Software
Authors: Bouzenia Islem , Krishan Bajaj Piyush , Pradel Michael
Published: July 2024

Python has emerged as one of the most popular programming languages, extensively utilized in domains such as machine learning, data analysis, and web applications. Python’s dynamic nature and extensive usage make it an attractive candidate for dynamic program analysis. However, unlike for other popular languages, there currently is no comprehensive benchmark suite of executable Python projects, which hinders the development of dynamic analyses. This work addresses this gap by presenting DyPyBench, the first benchmark of Python projects that is large-scale, diverse, ready-to-run (i.e., with fully configured and prepared test suites), and ready- to-analyze (by integrating with the DynaPyt dynamic analysis framework). The benchmark encompasses 50 popular open-source projects from various application domains, with a total of 681k lines of Python code, and 30k test cases. DyPyBench enables various applications in testing and dynamic analysis, of which we explore three in this work: (i) Gathering dynamic call graphs and empirically comparing them to statically computed call graphs, which exposes and quantifies limitations of existing call graph construction techniques for Python. (ii) Using DyPyBench to build a training data set for LExecutor, a neural model that learns to predict values that otherwise would be missing at runtime. (iii) Using dynamically gathered execution traces to mine API usage specifications, which establishes a baseline for future work on specification mining for Python. We envision DyPyBench to provide a basis for other dynamic analyses and for studying the runtime behavior of Python code.
Adapting Multi-objectivized Software Configuration Tuning
Authors: Chen Tao , Li Miqing
Published: July 2024

When tuning software configuration for better performance (e.g., latency or throughput), an important issue that many optimizers face is the presence of local optimum traps, compounded by a highly rugged configuration landscape and expensive measurements. To mitigate these issues, a recent effort has shifted to focus on the level of optimization model (called meta multi-objectivization or MMO) instead of designing better optimizers as in traditional methods. This is done by using an auxiliary performance objective, together with the target performance objective, to help the search jump out of local optima. While effective, MMO needs a fixed weight to balance the two objectives—a parameter that has been found to be crucial as there is a large deviation of the performance between the best and the other settings. However, given the variety of configurable software systems, the “sweet spot” of the weight can vary dramatically in different cases and it is not possible to find the right setting without time-consuming trial and error. In this paper, we seek to overcome this significant shortcoming of MMO by proposing a weight adaptation method, dubbed AdMMO. Our key idea is to adaptively adjust the weight at the right time during tuning, such that a good proportion of the nondominated configurations can be maintained. Moreover, we design a partial duplicate retention mechanism to handle the issue of too many duplicate configurations without losing the rich information provided by the “good” duplicates.

Experiments on several real-world systems, objectives, and budgets show that, for 71% of the cases, AdMMO is considerably superior to MMO and a wide range of state-of-the-art optimizers while achieving generally better efficiency with the best speedup between 2.2x and 20x.
Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example
Authors: Dilhara Malinda , Bellur Abhiram , Bryksin Timofey , Dig Danny
Published: July 2024

Software developers often repeat the same code changes within a project or across different projects. These repetitive changes are known as “code change patterns” (CPATs). Automating CPATs is crucial to expedite the software development process. While current Transformation by Example (TBE) techniques can automate CPATs, they are limited by the quality and quantity of the provided input examples. Thus, they miss transforming code variations that do not have the exact syntax, data-, or control-flow of the provided input examples, despite being semantically similar. Large Language Models (LLMs), pre-trained on extensive source code datasets, offer a potential solution. Harnessing the capability of LLMs to generate semantically equivalent, yet previously unseen variants of the original CPAT could significantly increase the effectiveness of TBE systems.

In this paper, we first discover best practices for harnessing LLMs to generate code variants that meet three criteria: correctness (semantic equivalence to the original CPAT), usefulness (reflecting what developers typically write), and applicability (aligning with the primary intent of the original CPAT). We then implement these practices in our tool PyCraft, which synergistically combines static code analysis, dynamic analysis, and LLM capabilities. By employing chain-of-thought reasoning, PyCraft generates variations of input examples and comprehensive test cases that identify correct variations with an F-measure of 96.6%. Our algorithm uses feedback iteration to expand the original input examples by an average factor of 58x. Using these richly generated examples, we inferred transformation rules and then automated these changes, resulting in an increase of up to 39x, with an average increase of 14x in target codes compared to a previous state-of-the-art tool that relies solely on static analysis. We submitted patches generated by PyCraft to a range of projects, notably esteemed ones like microsoft/DeepSpeed and IBM/inFairness. Their developers accepted and merged 83% the 86 CPAT instances submitted through 44 pull requests. This confirms the usefulness of these changes.
Understanding and Detecting Annotation-Induced Faults of Static Analyzers
Authors: Zhang Huaien , Pei Yu , Liang Shuyun , Tan Shin Hwei
Published: July 2024

Static analyzers can reason about the properties and behaviors of programs and detect various issues without executing them. Hence, they should extract the necessary information to understand the analyzed program well. Annotation has been a widely used feature for different purposes in Java since the introduction of Java 5. Annotations can change program structures and convey semantics information without awareness of static analyzers, consequently leading to imprecise analysis results. This paper presents the first comprehensive study of annotation-induced faults (AIF) by analyzing 246 issues in six open-source and popular static analyzers (i.e., PMD, SpotBugs, CheckStyle, Infer, SonarQube, and Soot). We analyzed the issues' root causes, symptoms, and fix strategies and derived ten findings and some practical guidelines for detecting and repairing annotation-induced faults. Moreover, we developed an automated testing framework called AnnaTester based on three metamorphic relations originating from the findings. AnnaTester generated new tests based on the official test suites of static analyzers and unveiled 43 new faults, 20 of which have been fixed. The results confirm the value of our study and its findings.
Harnessing Neuron Stability to Improve DNN Verification
Authors: Duong Hai , Xu Dong , Nguyen Thanhvu , Dwyer Matthew B.
Published: July 2024

Deep Neural Networks (DNN) have emerged as an effective approach to tackling real-world problems. However, like human-written software, DNNs are susceptible to bugs and attacks. This has generated significant interest in developing effective and scalable DNN verification techniques and tools. Recent developments in DNN verification have highlighted the potential of constraint-solving approaches that combine abstraction techniques with SAT solving. Abstraction approaches are effective at precisely encoding neuron behavior when it is linear, but they lead to overapproximation and combinatorial scaling when behavior is non-linear. SAT approaches in DNN verification have incorporated standard DPLL techniques, but have overlooked important optimizations found in modern SAT solvers that help them scale on industrial benchmarks. In this paper, we present VeriStable, a novel extension of the recently proposed DPLL-based constraint DNN verification approach. VeriStable leverages the insight that while neuron behavior may be non-linear across the entire DNN input space, at intermediate states computed during verification many neurons may be constrained to have linear behavior – these neurons are stable. Efficiently detecting stable neurons reduces combinatorial complexity without compromising the precision of abstractions. Moreover, the structure of clauses arising in DNN verification problems shares important characteristics with industrial SAT benchmarks. We adapt and incorporate multi-threading and restart optimizations targeting those characteristics to further optimize DPLL-based DNN verification. We evaluate the effectiveness of VeriStable across a range of challenging benchmarks including fully- connected feedforward networks (FNNs), convolutional neural networks (CNNs) and residual networks (ResNets) applied to the standard MNIST and CIFAR datasets. Preliminary results show that VeriStable is competitive and outperforms state-of-the-art DNN verification tools, including α-β-CROWN and MN-BaB, the first and second performers of the VNN-COMP, respectively.
“The Law Doesn’t Work Like a Computer”: Exploring Software Licensing Issues Faced by Legal Practitioners
Authors: Wintersgill Nathan , Stalnaker Trevor , Heymann Laura A. , Chaparro Oscar , Poshyvanyk Denys
Published: July 2024

Most modern software products incorporate open source components, which requires compliance with each component's licenses. As noncompliance can lead to significant repercussions, organizations often seek advice from legal practitioners to maintain license compliance, address licensing issues, and manage the risks of noncompliance. While legal practitioners play a critical role in the process, little is known in the software engineering community about their experiences within the open source license compliance ecosystem. To fill this knowledge gap, a joint team of software engineering and legal researchers designed and conducted a survey with 30 legal practitioners and related occupations and then held 16 follow-up interviews. We identified different aspects of OSS license compliance from the perspective of legal practitioners, resulting in 14 key findings in three main areas of interest: the general ecosystem of compliance, the specific compliance practices of legal practitioners, and the challenges that legal practitioners face. We discuss the implications of our findings.
Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge Distillation
Authors: Ben Sghaier Oussama , Sahraoui Houari
Published: July 2024

Code review is a fundamental process in software development that plays a pivotal role in ensuring code quality and reducing the likelihood of errors and bugs. However, code review can be complex, subjective, and time-consuming.
Quality estimation
,
comment generation
, and
code refinement
constitute the three key tasks of this process, and their automation has traditionally been addressed separately in the literature using different approaches. In particular, recent efforts have focused on fine-tuning pre-trained language models to aid in code review tasks, with each task being considered in isolation. We believe that these tasks are interconnected, and their fine-tuning should consider this interconnection. In this paper, we introduce a novel deep-learning architecture, named DISCOREV, which employs cross-task knowledge distillation to address these tasks simultaneously. In our approach, we utilize a cascade of models to enhance both
comment generation
and
code refinement
models. The fine-tuning of the
comment generation
model is guided by the
code refinement
model, while the fine-tuning of the
code refinement
model is guided by the
quality estimation
model. We implement this guidance using two strategies: a feedback-based learning objective and an embedding alignment objective. We evaluate DISCOREV by comparing it to state-of-the-art methods based on independent training and fine-tuning. Our results show that our approach generates better review comments, as measured by the
BLEU
score, as well as more accurate
code refinement
according to the
CodeBLEU
score.
DTD: Comprehensive and Scalable Testing for Debuggers
Authors: Lu Hongyi , Liu Zhibo , Wang Shuai , Zhang Fengwei
Published: July 2024

As a powerful tool for developers, interactive debuggers help locate and fix errors in software. By using

debugging information included in binaries, debuggers can retrieve necessary program states about the

program. Unlike printf-style debugging, debuggers allow for more flexible inspection and modification of

program execution states. However, debuggers may incorrectly retrieve and interpret program execution,

causing confusion and hindering the debugging process.

Despite the wide usage of interactive debuggers, a scalable and comprehensive measurement of their functionality correctness does not exist yet. Existing works either fall short in scalability or focus more on the

“compiler-side” defects instead of debugger bugs. To facilitate a better assessment of debugger correctness,

we first propose and advocate a set of debugger testing criteria, covering both comprehensiveness (in terms

of debug information covered) and scalability (in terms of testing overhead). Moreover, we design comparative experiments to show that fulfilling these criteria is not only theoretically appealing, but also brings

major improvement to debugger testing. Furthermore, based on these criteria, we present DTD, a differential

testing (DT) framework for detecting bugs in interactive debuggers. DTD compares the behaviors of two

mainstream debuggers when processing an identical C executable — discrepancies indicate bugs in one of the

two debuggers.

DTD leverages a novel heuristic method to avoid the repetitive structures (e.g., loops) that exist in C

programs, which facilitates DTD to achieve full debug information coverage efficiently. Moreover, we have

also designed a Temporal Differential Filtering method to practically filter out the false positives caused by

the uninitialized variables in common C programs. With these carefully designed techniques, DTD fulfills

our proposed testing requirements and, therefore, achieves high scalability and testing comprehensiveness.

For the first time, it offers large-scale testing for C debuggers to detect debugger behavior discrepancies

when inspecting millions of program states. An empirical comparison shows that DTD finds 17× more error-triggering cases and detects 5× more bugs than the state-of-the-art debugger testing technique. We have used DTD to detect 13 bugs in the LLVM toolchain (Clang/LLDB) and 5 bugs in the GNU toolchain (GCC/GDB).

One of our fixes has already landed in the latest LLDB development branch.
Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications
Authors: Jayasuriya Dhanushka , Terragni Valerio , Dietrich Jens , Blincoe Kelly
Published: July 2024

Libraries play a significant role in software development as they provide reusable functionality, which helps expedite the development process. As libraries evolve, they release new versions with optimisations like new functionality, bug fixes, and patches for known security vulnerabilities. To obtain these optimisations, the client applications that depend on these libraries must update to use the latest version. However, this can cause software failures in the clients if the update includes breaking changes. These breaking changes can be divided into syntactic and semantic (behavioral) breaking changes. While there has been considerable research on syntactic breaking changes introduced between library updates and their impact on client projects, there is a notable lack of research regarding behavioral breaking changes introduced during library updates and their impacts on clients.

We conducted an empirical analysis to identify the impact behavioral breaking changes have on clients by examining the impact of dependency updates on client test suites. We examined a set of java projects built using Maven, which included 30,548 dependencies under 8,086 Maven artifacts. We automatically updated out-of-date dependencies and ran the client test suites. We found that 2.30% of these updates had behavioral breaking changes that impacted client tests. Our results show that most breaking changes were introduced during a non-Major dependency update, violating the semantic versioning scheme. We further analyzed the effects these behavioral breaking changes have on client tests. We present a taxonomy of effects related to these changes, which we broadly categorize as Test Failures and Test Errors. Our results further indicate that the library developers did not adequately document the exceptions thrown due to precondition violations.
A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-Based Anomaly Detection Techniques
Authors: Landauer Max , Skopik Florian , Wurzenberger Markus
Published: July 2024

Log data store event execution patterns that correspond to underlying workflows of systems or applications. While most logs are informative, log data also include artifacts that indicate failures or incidents. Accordingly, log data are often used to evaluate anomaly detection techniques that aim to automatically disclose unexpected or otherwise relevant system behavior patterns. Recently, detection approaches leveraging deep learning have increasingly focused on anomalies that manifest as changes of sequential patterns within otherwise normal event traces. Several publicly available data sets, such as HDFS, BGL, Thunderbird, OpenStack, and Hadoop, have since become standards for evaluating these anomaly detection techniques, however, the appropriateness of these data sets has not been closely investigated in the past. In this paper we therefore analyze six publicly available log data sets with focus on the manifestations of anomalies and simple techniques for their detection. Our findings suggest that most anomalies are not directly related to sequential manifestations and that advanced detection techniques are not required to achieve high detection rates on these data sets.
On the Contents and Utility of IoT Cybersecurity Guidelines
Authors: Chen Jesse , Anandayuvaraj Dharun , Davis James C. , Rahaman Sazzadur
Published: July 2024

Cybersecurity concerns of Internet of Things (IoT) devices and infrastructure are growing each year. In response, organizations worldwide have published IoT security guidelines to protect their citizens and customers by providing recommendations on the development and operation of IoT systems. While these guidelines are being adopted, e.g. by US federal contractors, their content and merits have not been critically examined. Specifically, we do not know what topics and recommendations they cover and their effectiveness at preventing real-world IoT failures.

In this paper, we address these gaps through a qualitative study of guidelines. We collect 142 IoT cybersecurity guidelines and sample them for recommendations until reaching saturation at 25 guidelines. From the resulting 958 unique recommendations, we iteratively develop a hierarchical taxonomy following grounded theory coding principles and study the guidelines’ comprehensiveness. In addition, we evaluate the actionability and specificity of each recommendation and match recommendations to CVEs and security failures in the news they can prevent. We report that: (1) Each guideline has gaps in its topic coverage and comprehensiveness; (2) 87.2% recommendations are actionable and 38.7% recommendations can prevent specific threats; and (3) although the union of the guidelines mitigates all 17 of the failures from our news stories corpus, 21% of the CVEs evade the guidelines. In summary, we report shortcomings in each guideline’s depth and breadth, but as a whole they address major security issues.
A Deep Dive into Large Language Models for Automated Bug Localization and Repair
Authors: Hossain Soneya Binta , Jiang Nan , Zhou Qiang , Li Xiaopeng , Chiang Wen-Hao , Lyu Yingjun , Nguyen Hoan , Tripp Omer
Published: July 2024

Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks,

including automated program repair (APR). In this study, we take a deep dive into automated bug localization

and repair utilizing LLMs. In contrast to many deep learning-based APR methods that assume known bug

locations, rely on line-level localization tools, or address bug prediction and fixing in one step, our approach

uniquely employs LLMs to predict bug location at the token level and subsequently utilizes them for bug

fixing. This methodological separation of bug localization and fixing using different LLMs enables effective

integration of diverse contextual information and improved incorporation of inductive biases. We introduce

Toggle: Token-Granulated Bug Localization and Repair, a comprehensive program repair framework

that integrates a bug localization model, an adjustment model to address tokenizer inconsistencies, and a

bug-fixing model. Toggle takes a buggy function as input and generates a complete corrected function. We

investigate various styles of prompting to the bug fixing model to identify the most effective prompts that

better utilize the inductive bias and significantly outperform others. Toggle achieves the new state-of-the-art

(SOTA) performance on the CodeXGLUE code refinement benchmark, and exhibits better and comparable

performance on several other widely-used APR datasets, including Defects4J. In the Defects4J benchmark, our

approach consistently ranks above other methods, achieving superior results in the Top-10, Top-30, Top-50,

and Top-100 metrics. Besides examining Toggle’s generalizability to unseen data, evaluating the effectiveness

of various prompts, we also investigate the impact of additional contextual information such as buggy lines

and code comments on bug localization, and explore the importance of the adjustment model. Our extensive

experiments offer valuable insights and answers to critical research questions.
On Reducing Undesirable Behavior in Deep-Reinforcement-Learning-Based Software
Authors: Carmel Ophir M. , Katz Guy
Published: July 2024

Deep reinforcement learning (DRL) has proven extremely useful in a large variety of application domains. However, even successful DRL-based software can exhibit highly undesirable behavior. This is due to DRL training being based on maximizing a reward function, which typically captures general trends but cannot precisely capture, or rule out, certain behaviors of the model. In this paper, we propose a novel framework aimed at drastically reducing the undesirable behavior of DRL-based software, while maintaining its excellent performance. In addition, our framework can assist in providing engineers with a comprehensible characterization of such undesirable behavior. Under the hood, our approach is based on extracting decision tree classifiers from erroneous state-action pairs, and then integrating these trees into the DRL training loop, penalizing the model whenever it performs an error. We provide a proof-of-concept implementation of our approach, and use it to evaluate the technique on three significant case studies. We find that our approach can extend existing frameworks in a straightforward manner, and incurs only a slight overhead in training time. Further, it incurs only a very slight hit to performance, or even in some cases --- improves it, while significantly reducing the frequency of undesirable behavior.
CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection
Authors: Dou Shihan , Wu Yueming , Jia Haoxiang , Zhou Yuhao , Liu Yan , Liu Yang
Published: July 2024

With the development of the open source community, the code is often copied, spread, and evolved in multiple software systems, which brings uncertainty and risk to the software system (e.g., bug propagation and copyright infringement). Therefore, it is important to conduct code clone detection to discover similar code pairs. Many approaches have been proposed to detect code clones where token-based tools can scale to big code. However, due to the lack of program details, they cannot handle more complicated code clones, i.e., semantic code clones. In this paper, we introduce CC2Vec, a novel code encoding method designed to swiftly identify simple code clones while also enhancing the capability for semantic code clone detection. To retain the program details between tokens, CC2Vec divides them into different categories (i.e., typed tokens) according to the syntactic types and then applies two self-attention mechanism layers to encode them. To resist changes in the code structure of semantic code clones, CC2Vec performs contrastive learning to reduce the differences introduced by different code implementations. We evaluate CC2Vec on two widely used datasets (i.e., BigCloneBench and Google Code Jam) and the results report that our method can effectively detect simple code clones. In addition, CC2Vec not only attains comparable performance to widely used semantic code clone detection systems such as ASTNN, SCDetector, and FCCA by simply fine-tuning, but also significantly surpasses these methods in both detection efficiency.