IEEE Transactions on Software Engineering最新文献_第3页

Enhancing Blockchain Robustness Through Efficient Feedback-Driven Chaos Engineering 基于高效反馈驱动混沌工程增强区块链鲁棒性

IF 6 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-10-01 Epub Date: 2026-07-03 DOI: 10.1109/TSE.2026.3709769

Junjie Ma;Muhui Jiang;Edmond W. W. Chan;Xiapu Luo;Qi Wang;Fengwei Zhang

{"title":"Enhancing Blockchain Robustness Through Efficient Feedback-Driven Chaos Engineering","authors":"Junjie Ma;Muhui Jiang;Edmond W. W. Chan;Xiapu Luo;Qi Wang;Fengwei Zhang","doi":"10.1109/TSE.2026.3709769","DOIUrl":"https://doi.org/10.1109/TSE.2026.3709769","url":null,"abstract":"The rapid evolution of blockchain technology has attracted global users to operate nodes and participate in consensus processes. However, heterogeneous networks and hardware conditions may induce chaotic behavior in nodes, leading to critical faults such as packet loss or data corruption. These faults compromise system robustness, causing node crashes or desynchronization. Existing chaos engineering methods for blockchain systems suffer from inefficient fault injection and lack feedback mechanisms to iteratively guide fault selection. To address these limitations, we propose ChaosChain, a framework for efficient and effective chaos engineering in blockchain systems to expose robustness vulnerabilities. For efficiency, we design a parallel fault coordination mechanism that injects multiple fault types into arbitrary nodes simultaneously while enforcing blockchain consensus security constraints. We construct a global timeline of all executed faults and validate security constraints before each injection. To improve effectiveness, we introduce next fault guidance, a feedback-driven approach that selects optimal faults to trigger complex node state transitions. We model each node’s state as an interference-resistant state machine that captures state transitions during faults without cross-node interference. We evaluate ChaosChain across four implementations in Filecoin and Ethereum, uncovering six new robustness issues. Compared to state-of-the-art tools, our framework injects 4<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> more faults concurrently.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"3090-3105"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ConcernBERT: Learning Responsibilities Using Class Membership 关注：利用班级成员学习责任

IF 6 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-10-01 Epub Date: 2026-06-26 DOI: 10.1109/TSE.2026.3707727

Jason Lefever;Jiahao Xu;Yuanfang Cai;Rick Kazman;Ernst Pisch

{"title":"ConcernBERT: Learning Responsibilities Using Class Membership","authors":"Jason Lefever;Jiahao Xu;Yuanfang Cai;Rick Kazman;Ernst Pisch","doi":"10.1109/TSE.2026.3707727","DOIUrl":"10.1109/TSE.2026.3707727","url":null,"abstract":"The principles of <italic>separation of concerns, <italic>high cohesion, and <italic>single responsibility are among the most well-known in software design. However, their application often remains philosophical rather than actionable, relying heavily on developers’ intuition and experience. Many software tasks, such as god class decomposition, extract class refactoring, and cohesion measurement, depend on techniques for identifying cohesive groups of program entities, that is, entities that collectively fulfill a common responsibility. Yet reliably identifying such groups remains a challenge. In this paper, we propose ConcernBERT, a BERT-based embedding model trained at the entity level that uses triplet loss to directly optimize the relative positioning of methods and attributes in the embedding space, and uses class-membership context to learn responsibilities and concerns. We also contribute a large-scale replication dataset for training and evaluation. Our dataset spans over two million Java files across more than six thousand repositories. To evaluate ConcernBERT, we merge methods from two or more classes into unlabeled groups and test the model’s ability to recover the original class memberships. ConcernBERT achieves significantly higher performance than existing models, demonstrating its effectiveness at encoding concern-level semantics and establishing a strong foundation for downstream tasks such as architecture recovery, extract class refactoring, and cohesion measurement.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"3040-3054"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148342292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Time Efficiency of Code Generated and Optimized by Large Language Models 基于大型语言模型的代码生成与优化的时间效率

IF 6 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-10-01 Epub Date: 2026-07-03 DOI: 10.1109/TSE.2026.3709872

Yun Peng;Jun Wan;Xiaoxue Ren;Tim Menzies;Michael R. Lyu

{"title":"On the Time Efficiency of Code Generated and Optimized by Large Language Models","authors":"Yun Peng;Jun Wan;Xiaoxue Ren;Tim Menzies;Michael R. Lyu","doi":"10.1109/TSE.2026.3709872","DOIUrl":"https://doi.org/10.1109/TSE.2026.3709872","url":null,"abstract":"Code generation has largely improved development efficiency in the era of large language models (LLMs). With the ability to follow instructions, current LLMs can be prompted to generate and optimize code solutions given detailed descriptions in natural language. Many research efforts are devoted to improving the correctness of LLM-generated code by proposing different benchmarks. Despite the focus on correctness, the time efficiency of LLM-generated code solutions is underexplored. Current correctness benchmarks are not suitable for time efficiency evaluation since their test cases cannot well distinguish the time efficiency of different code solutions. Besides, the current execution time measurement is not stable and comprehensive, threatening the validity of the time efficiency evaluation. To address the challenges in the time efficiency evaluation, we propose <sc>Coffe, a benchmark for evaluating the time efficiency of LLM-generated code solutions in code generation and code optimization. <sc>Coffe contains 398 and 358 problems for function-level and file-level code generation, respectively. To improve the distinguishability, we design a novel stressful test case generation approach with contracts and two new formats of test cases to improve the accuracy of generation. To improve the robustness, we also design a test case input scale mutation method to generate test cases with different input scales. For the time evaluation metric, we propose efficienct@k based on CPU instruction count to ensure a stable and solid comparison between different solutions. We evaluate 19 popular LLMs and 7 LLM-based code optimization methods on <sc>Coffe and identify 10 findings. Based on the findings, we draw some implications for LLM researchers and software practitioners to facilitate future research and usage of LLMs in code generation and optimization.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"3106-3128"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DPSSX: Detecting and Preventing SQLi and Stored XSS Induced by Expressions in Web Application SQL Statements via Static-Dynamic Analysis DPSSX：通过静态-动态分析检测和预防Web应用SQL语句中表达式引起的SQLi和存储的XSS

IF 6 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-10-01 Epub Date: 2026-07-01 DOI: 10.1109/TSE.2026.3709280

Bing Zhang;Hongbo Jin;Rong Ren;Jiadong Ren;Qian Wang

{"title":"DPSSX: Detecting and Preventing SQLi and Stored XSS Induced by Expressions in Web Application SQL Statements via Static-Dynamic Analysis","authors":"Bing Zhang;Hongbo Jin;Rong Ren;Jiadong Ren;Qian Wang","doi":"10.1109/TSE.2026.3709280","DOIUrl":"https://doi.org/10.1109/TSE.2026.3709280","url":null,"abstract":"Injection vulnerabilities in SQL statements (e.g., SQLi and stored XSS) critically threaten web applications. Existing detection methods—source-origin-based, structure-based, and variable/rule-based—struggle when SQL statements contain arithmetic or function expressions, resulting in high false positives. To overcome this, we propose DPSSX, an automated detection and prevention prototype leveraging static and dynamic analysis to <underline>Detect and <underline>Prevent <underline>SQLi and <underline>Stored <underline>XSS attacks triggered by expressions in SQL statements. DPSSX first applies static analysis to locate SQL statements, identify data sources (POST, sessions, databases, etc.), and flag variables in arithmetic/function expressions that may alter attack impact. A dynamic global HTTP request extractor then collects comprehensive inputs for complex applications. Using a joint attack engine, DPSSX injects payloads covering the latest attack patterns. Through proxy-based execution, it captures SQL queries containing payloads or expressions. An expression pre-execution module computes results to mitigate false positives from expression-based neutralization/amplification. By matching query data against attack-pattern rules, DPSSX detects first/second-order SQL injection and stored XSS attacks and sanitizes malicious inputs using character escaping, truncation, unified decoding, and malicious keyword blocking. In our evaluation, using five attack generators to produce 120,495 SQL injection attacks and 3,763 XSS attacks, DPSSX successfully identified 441 first-order SQL injection vulnerabilities, 28 second-order SQL injection vulnerabilities, and 101 stored XSS vulnerabilities across 20 well-known open-source web applications. Additionally, DPSSX achieved 100% elimination of malicious effects on data with negligible overhead.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"3071-3089"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GATES: Accelerating Solving-Based Testing Through Adaptive Solving and Guided Execution 盖茨：通过自适应解决和引导执行加速基于解决的测试

IF 6 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-10-01 Epub Date: 2026-07-08 DOI: 10.1109/TSE.2026.3708192

Haowei Qiu;Zhuo Su;Zehong Yu;Xinhe Tong;Dongyi Yu;Yu Jiang

{"title":"GATES: Accelerating Solving-Based Testing Through Adaptive Solving and Guided Execution","authors":"Haowei Qiu;Zhuo Su;Zehong Yu;Xinhe Tong;Dongyi Yu;Yu Jiang","doi":"10.1109/TSE.2026.3708192","DOIUrl":"10.1109/TSE.2026.3708192","url":null,"abstract":"Simulink models are widely adopted in embedded systems development, where rigorous model testing plays an important role in ensuring system reliability. However, efficiently achieving high model test coverage remains a significant challenge due to vast state spaces and intricate state-dependent logic. Although solving-based test case generation is a powerful method, redundant solving often becomes a performance bottleneck. This leads to significant overhead that limits its practical applicability. This paper introduces GATES, which employs adaptive solving to address valuable tasks and then incorporates guided execution to cover branches more efficiently. Specifically, GATES uses an asynchronous unreachable branch filter to proactively prune the search space. For solvable tasks, the dynamic priority solving task queue is employed to raise the priority of valuable tasks that are capable of covering more branches. After that, GATES duplicates and assembles solved inputs for guided execution. We evaluate GATES on a suite of industrial benchmark models. Compared with the commercial tool SLDV, GATES achieves average improvements of 89.0%, 93.5%, and 330.3% in Decision Coverage, Condition Coverage, and Modified Condition/Decision Coverage (MCDC), respectively. Furthermore, compared to the recent academic tool HSTCG, GATES achieves higher MCDC while reducing the average time to reach peak coverage by 70.0%.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"3055-3070"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Non-Optimal Decisions of Embodied Agents via Diversity-Guided Metamorphic Testing 利用多样性引导的变形测试检测具身代理的非最优决策

IF 7.4 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-09-22 DOI: 10.1109/tse.2026.3736694

Wenzhao Wu, Yahui Tang, Mingfei Cheng, Wenbing Tang, Yuan Zhou, Yang Liu

引用次数: 0

Improved V-Method and Its Tool Support for Specification-Based Testing 改进的V-Method及其对基于规范测试的工具支持

IF 7.4 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-09-21 DOI: 10.1109/tse.2026.3736434

Shaoying Liu, Yanzhao Xia, Yu Du, Hiroyuki Okamura, Tadashi Dohi

引用次数: 0

FlakyAssessor: Static Assessment of Patch Correctness for Flaky Tests FlakyAssessor：对片状测试的补丁正确性进行静态评估

IF 7.4 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-09-08 DOI: 10.1109/tse.2026.3732261

Zhenyu Wu, Zheyuan Li, Yan Lei, Deheng Yang, Junyi Li, Huan Xie, Chunyan Liu

引用次数: 0

Agent System Operations: Categorization, Challenges, and Future Directions 代理系统操作：分类、挑战和未来方向

IF 7.4 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-09-08 DOI: 10.1109/tse.2026.3732207

Zexin Wang, Changhua Pei, Yuanhao Liu, Jingjing Li, Yintong Huo, Quan Zhou, Haotian Si, Hang Cui, Zihan Liu, Jianhui Li, Gaogang Xie, Fei Sun, Dan Pei, David Lo

引用次数: 0

How Developers Respond to Insecure AI Code Suggestions under Poisoning Attacks: An Empirical Study of Interface-Level Countermeasures 开发者如何应对投毒攻击下的不安全AI代码建议：接口级对策的实证研究

IF 7.4 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2026-09-07 DOI: 10.1109/tse.2026.3731966

Sanghak Oh, Eun Jung, Sangjun Chae, Hyoungshick Kim

引用次数: 0