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39th ACM/IEEE international conference on Automated software engineering Workshops, ASEW 2024

作者： Alshabeeb, Esra'a Alshammare, Haifa Alakkas, Noor Alshayeb, Mohammad Faisal University Imam Abdulrahman Bin Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia Digital Technical College for Girls Technical and Vocational Training Corporation Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia Faisal University Imam Abdulrahman Bin Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia

ISBN: (纸本)9798400712494

Despite the importance of adopting SPM tools for supporting agile practices and the significance of usability as an essential component of human factors engineering, their usability evaluation was neglected in the literature. This study aims to identify the common usability issues in SPM tools and propose suggestions for enhancing their usability. Two of the most widely used SPM tools were selected as a case study for this study, namely Jira and Pivotal Tracker. Three independent evaluators participated in the usability evaluation, using two human factors techniques: Heuristic Evaluation and Cognitive Walkthrough. The results revealed a total frequency of 123 issues in both tools. Both tools had a nearly equal frequency of issues in each evaluation method but different severity levels. Learnability issues were the issues that were raised the most in the evaluation for both tools. Most of the issues had minor to major severity, with the following percentages of total issues: 41.33% and 46.67%, respectively, while only 13.33% were catastrophic. Considering the proposed solutions by designers and developers for this type of tool can help enhance the design of SPM tools, leading to increased user satisfaction and tool adoption. © 2024 ACM.

关键词： Project management

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DevMuT: Testing Deep Learning Framework via Developer Expertise-Based Mutation 24

DevMuT: Testing Deep Learning Framework via Developer Expert...

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39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Mu, Yanzhou Zhai, Juan Fang, Chunrong Chen, Xiang Cao, Zhixiang Yang, Peiran Zou, Yinglong Zheng, Tao Chen, Zhenyu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Massachusetts Amherst Amherst MA USA Nantong Univ Sch Artificial Intelligence & Comp Sci Sch Zhang Jian Nantong Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Deep learning (DL) frameworks are the fundamental infrastructure for various DL applications. Framework defects can profoundly cause disastrous accidents, thus requiring sufficient detection. In previous studies, researchers adopt DL models as test inputs combined with mutation to generate more diverse models. Though these studies demonstrate promising results, most detected defects are considered trivial (i.e., either treated as edge cases or ignored by the developers). To identify important bugs that matter to developers, we propose a novel DL framework testing method DevMuT, which generates models by adopting mutation operators and constraints derived from developer expertise. DevMuT simulates developers' common operations in development and detects more diverse defects within more stages of the DL model lifecycle (e.g., model training and inference). We evaluate the performance of DevMuT on three widely used DL frameworks (i.e., PyTorch, JAX, and Mind-Spore) with 29 DL models from nine types of industry tasks. The experiment results show that DevMuT outperforms state-of-the-art baselines: it can achieve at least 71.68% improvement on average in the diversity of generated models and 28.20% improvement on average in the legal rates of generated models. Moreover, DevMuT detects 117 defects, 63 of which are confirmed, 24 are fixed, and eight are of high value confirmed by developers. Finally, DevMuT has been deployed in the MindSpore community since December 2023. These demonstrate the effectiveness of DevMuT in detecting defects that are close to the real scenes and are of concern to developers.

关键词： Training Deep learning Industries Law Image edge detection computer bugs Testing software engineering Accidents

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CPLS: Optimizing the Assignment of LLM Queries 40

CPLS: Optimizing the Assignment of LLM Queries

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40th international conference on software Maintenance and Evolution

作者： Liu, Yueyue Zhang, Hongyu Li, Zhiqiang Miao, Yuantian Univ Newcastle Sch Informat & Phys Sci Newcastle NSW Australia Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9798350395693;9798350395686

Large Language Models (LLMs) like ChatGPT have gained significant attention because of their impressive capabilities, leading to a dramatic increase in their integration into intelligent software engineering. However, their usage as a service with varying performance and price options presents a challenging trade-off between desired performance and the associated cost. To address this challenge, we propose CPLS, a framework that utilizes transfer learning and local search techniques for assigning intelligent software engineering jobs to LLM-based services. CPLS aims to minimize the total cost of LLM invocations while maximizing the overall accuracy. The framework first leverages knowledge from historical data across different projects to predict the probability of an LLM processing a query correctly. Then, CPLS incorporates problem-specific rules into a local search algorithm to effectively generate Pareto optimal solutions based on the predicted accuracy and cost. To evaluate the proposed approach, we conduct extensive experiments on LLM-based log parsing, a typical software maintenance task. Our experimental results demonstrate that CPLS outperforms the baseline methods, providing solutions with the highest accuracy in 14 out of 16 instances. Compared to the baselines, CPLS achieves an accuracy improvement ranging from 1.24% to 485.54%, or reduces costs by 15.21% to 89.09% while maintaining the highest accuracy achieved by the baselines.

关键词： Large Language Models Query Assignment Cross-project Prediction Local Search Log Parsing

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Analysis of software engineering Practices in General software and Machine Learning Startups 21

Analysis of Software Engineering Practices in General Softwa...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Lakha, Bishal Bhetwal, Kalyan Eisty, Nasir U. Boise State University Computer Science Department BoiseID United States

ISBN: (纸本)9798350345889

Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products. © 2023 IEEE.

关键词： Machine learning

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IG-DP: Graph Neural Network-based Default Prediction Method for Imbalanced Financial Datasets

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第4期52卷 965-973页

作者： Wang, Changhai Ren, Jiaxi Huang, Min Liu, Qing Xu, Yaoli Software Engineering College Zhengzhou University of Light Industry Zhengzhou450000 China Software Engineering College Zhengzhou University of Light Industry Zhengzhou450000 China Software Engineering College Zhengzhou University of Light Industry Zhengzhou450000 China Electronics and Information Engineering West Anhui University Anhui Lu’an237012 China Software Engineering College Zhengzhou University of Light Industry Zhengzhou450000 China

Predicting financial defaults plays a vital role in reducing financial risks for credit businesses. A prominent trend in this area is the incorporation of borrowers’ social profiles into predictive models using graph neural networks. However, the significant imbalance between default and normal users in labeled financial datasets poses challenges for training effective prediction models, often leading to overfitting. A financial default prediction model named IG-DP is proposed in this paper, which is designed to use unlabeled background nodes to enhance prediction performance. First, imbalanced labeled samples are used to train an initial graph neural network classifier, and the node embeddings of the unlabeled samples can be obtained with the initial model. Then, background nodes proximal to the labeled samples are selected for pseudo labeling with the similarity selection module based on the embedding vector of the node. Finally, the pseudo-labeled samples are added to the training set to retrain the prediction model. The DGraphFin dataset is used for experimental evaluation, and the AUC−ROC and F1−measure are chosen as evaluation metrics. The experimental results demonstrate that IG-DP significantly outperforms other methods. Meanwhile, ablation experiments confirm the effectiveness of both the similarity selection module and the retraining loss calculation method for fusion confidence. © (2025.0), (international Association of Engineers). All Rights Reserved.

关键词： Prediction models

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Hyperparameter Optimization of software Quality Model Using Improvised Genetic Algorithm and EEM 1

Hyperparameter Optimization of Software Quality Model Using ...

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1st IEEE international conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rakhi Sharma, Vishnu Kaushik, Neeraj Malhotra, Ritu Rauthan, Hemlata Delhi Institute of Higher Education Department of Computer Application Greater Noida India ITS Engineering College Department Of Computer Science and Engineering Greater Noida India Galgotias College of Engineering and Tehnology Department of Computer Applications Greater Noida India GNIOT Institute of Professional Studies Department of Computer Applications Greater Noida India Delhi Institute of Higher Education Department of Computer Applications Greater Noida India

ISBN: (纸本)9798350356816

software quality analysis has become one of the topmost concerns in the modern world and requires the massive attention of developers and testers during software development. software quality analysis is a multidimensional notion and one attribute may not describe the software product's overall quality. software developer's objective is to build a software product that attains the highest quality which depends on multifarious attributes namely functional suitability, maintainability, etc. However, analysis of software product quality is challenging in both the development and maintenance phases. This research is aimed at hyperparameter optimization of the software quality model using an improvised genetic algorithm and an enhanced ensemble model (EEM) approach. In this research, there has been shown the optimal possibility of software quality model hyperparameter optimization using the modified architecture of genetic algorithm along with EEM. To validate the performance of this suggested approach, PROMISE Repository datasets have been utilized. As a result, this proposed approach achieves all performance metrics very optimal. The evaluated values of accuracy, precision, recall, and F1- score are 99.45%, 98.83%, 97.28%, and 99.37%, respectively. In the future, this approach may be integrated with modified architectures of software quality models for automated software product quality analysis. © 2024 IEEE.

关键词： software quality

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Behavior-Driven Specification in Practice: An Experience Report 30th

Behavior-Driven Specification in Practice: An Experience Rep...

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30th international Working conference on Requirements engineering - Foundation for software Quality (REFSQ)

作者： Allred, Joel D. Fraser, Simon Pezzoni, Alessandro Anaplan Ltd York N Yorkshire England

ISBN: (纸本)9783031573262;9783031573279

Agile methods are now widely used in software engineering organizations, whereas most formal methods are limited to niches and are perceived as inadequate in the context of agile development. This paper presents a case study of the innovative practices used at Anaplan, a financial planning and analysis software provider, to integrate formal specification within an agile process. The results show how Behavior-Driven Specification (BDS), by documenting behavior using executable acceptance criteria (EAC), is used to validate the design and implementation of calculation functions in Anaplan's sparse calculation engine, while keeping all stakeholders aligned on the requirements. We also show that the interaction between the specifiers and the developers allows catching implementation issues at early stages of the development, while allowing the specification to remain amenable to emerging implementation constraints. The validated requirements have enabled the development of a framework to automatically generate extensive test coverage that is used to verify the implementation. As a result, over 200 bugs were caught in the production code before release, not counting the hundreds of issues that BDS allowed developers to detect earlier in the process. We show that BDS leads to high levels of confidence in the behavioral correctness of software while being fully aligned with agile practices, and proves to be a significant evolution in the field of software development.

关键词： Behavior-driven specification Agile software development Requirements engineering

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Broken Windows: Exploring the Applicability of a Controversial Theory on Code Quality 40

Broken Windows: Exploring the Applicability of a Controversi...

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40th international conference on software Maintenance and Evolution

作者： Spinellis, Diomidis Louridas, Panos Kechagiadis, Maria Sharma, Tushar Athens Univ Econ & Business Athens Greece UCL London England Dalhousie Univ Halifax NS Canada

ISBN: (纸本)9798350395693;9798350395686

Is the quality of existing code correlated with the quality of subsequent changes? According to the (controversial) broken windows theory, which inspired this study, disorder sets descriptive norms and signals behavior that further increases it. From a large code corpus, we examine whether code history does indeed affect the evolution of code quality. We examine C code quality metrics and Java code smells in specific files, and see whether subsequent commits by developers continue on that path. We check whether developers tailor the quality of their commits based on the quality of the file they commit to. Our results show that history matters, that developers behave differently depending on some aspects of the code quality they encounter, and that programming style inconsistency is not necessarily related to structural qualities. These findings have implications for both software practice and research. software practitioners can emphasize current quality practices as these influence the code that will be developed in the future. Researchers in the field may replicate and extend the study to improve our understanding of the theory and its practical implications on artifacts, processes, and people.

关键词： code quality software evolution broken windows mining software repositories software analytics empirical study software smells

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Towards Demystifying Android Adware: Dataset and Payload Location 39

Towards Demystifying Android Adware: Dataset and Payload Loc...

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39th ACM/IEEE international conference on Automated software engineering Workshops, ASEW 2024

作者： Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China

ISBN: (纸本)9798400712494

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices. © 2024 ACM.

关键词： Android malware

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A First Look at the Inheritance-Induced Redundant Test Execution 24

A First Look at the Inheritance-Induced Redundant Test Execu...

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44th ACM/IEEE international conference on software engineering, ICSE 2024

作者： Kim, Dong Jae Chen, Tse-Hsun Peter Yang, Jinqiu Lab Concordia University MontrealQC Canada Concordia University Department of Computer Science and Software Engineering MontrealQC Canada

ISBN: (纸本)9798400702174

Inheritance, a fundamental aspect of object-oriented design, has been leveraged to enhance code reuse and facilitate efficient software development. However, alongside its benefits, inheritance can introduce tight coupling and complex relationships between classes, posing challenges for software maintenance. Although there are many studies on inheritance in source code, there is limited study on using inheritance in test code. In this paper, we take the first step by studying inheritance in test code, with a focus on redundant test executions caused by inherited test cases. We empirically study the prevalence of test inheritance and its characteristics. We also propose a hybrid approach that combines static and dynamic analysis to identify and locate inheritance-induced redundant test cases. Our findings reveal that (1) inheritance is widely utilized in the test code, (2) inheritance-induced redundant test executions are prevalent, accounting for 13% of all execution test cases, (3) bypassing these redundancies can help reduce 14% of the test execution time, and finally, (4) our study highlights the need for careful refactoring decisions to minimize redundant test cases and identifies the need for further research on test code quality. © 2024 ACM.

关键词： software testing

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