IEEE Transactions on Software Engineering最新文献

{"title":"An Empirical Study on Meta Virtual Reality Applications: Security and Privacy Perspectives","authors":"Hanyang Guo;Hong-Ning Dai;Xiapu Luo;Gengyang Xu;Fengliang He;Zibin Zheng","doi":"10.1109/TSE.2025.3553283","DOIUrl":"10.1109/TSE.2025.3553283","url":null,"abstract":"Virtual Reality (VR) has accelerated its prevalent adoption in emerging metaverse applications, but it is not a fundamentally new technology. On the one hand, most VR operating systems (OS) are based on off-the-shelf mobile OS (e.g., Android OS). As a result, VR apps also inevitably inherit privacy and security deficiencies from conventional mobile apps. On the other hand, in contrast to traditional mobile apps, VR apps can achieve an immersive experience via diverse VR devices, such as head-mounted displays, body sensors, and controllers. However, achieving this requires the extensive collection of privacy-sensitive human biometrics (e.g., hand-tracking and face-tracking data). Moreover, VR apps have been typically implemented by 3D gaming engines (e.g., Unity), which also contain intrinsic security vulnerabilities. Inappropriate use of these technologies may incur privacy leaks and security vulnerabilities although these issues have not received significant attention compared to the proliferation of diverse VR apps. In this paper, we develop a security and privacy assessment tool, namely the VR-SP detector for VR apps. The VR-SP detector has integrated program static analysis tools and privacy-policy analysis methods. Using the VR-SP detector, we conduct a comprehensive empirical study on 900 popular VR apps. We obtain the original apps from the popular SideQuest app store and extract Android PacKage (APK) files via the Meta Quest 2 device. We evaluate the security vulnerabilities and privacy data leaks of these VR apps through VR app analysis, taint analysis, privacy policy analysis, and user review analysis. We find that a number of security vulnerabilities and privacy leaks widely exist in VR apps. Moreover, our results also reveal conflicting representations in the privacy policies of these apps and inconsistencies of the actual data collection with the privacy-policy statements of the apps. Further, user reviews also indicate their privacy concerns about relevant biometric data. Based on these findings, we make suggestions for the future development of VR apps.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 5","pages":"1437-1454"},"PeriodicalIF":6.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661512","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}

{"title":"Adapting Installation Instructions in Rapidly Evolving Software Ecosystems","authors":"Haoyu Gao;Christoph Treude;Mansooreh Zahedi","doi":"10.1109/TSE.2025.3552614","DOIUrl":"10.1109/TSE.2025.3552614","url":null,"abstract":"README files play an important role in providing installation-related instructions to software users and are widely used in open source software systems on platforms such as GitHub. Software projects evolve rapidly alongside their dependencies in dynamic software ecosystems, requiring frequent updates to installation instructions. These instructions are crucial for users to start with a software project. Despite their significance, there is a lack of systematic understanding regarding the documentation efforts invested in README files and the triggers behind them. To fill the research gap, we conducted a qualitative study, investigating 400 GitHub repositories with 1,163 README commits that focused on updates in installation-related sections. Our research revealed six major categories of changes in the README commits, namely pre-installation instructions, installation instructions, post-installation instructions, help information updates, document presentation, and external resource management. We further provide detailed insights into modification behaviours and offer examples of these updates. We also studied the triggers for the documentation updates, which led to three categories including errors in the previous documentation, changes in the codebase, and need for documentation improvement. Based on our findings, we proposed a README template tailored to cover the installation-related sections for documentation maintainers to reference when updating documents. We further validated this template by conducting an online survey and a pull request study, identifying that documentation readers find the augmented documents based on our template to be generally of better quality, and documentation maintainers find it useful. We further provide recommendations to practitioners for maintaining their README files, as well as motivations for future research directions. These recommendations encompass completeness, correctness and up-to-dateness, and information presentation considerations. The proposed research directions include the development of automated tools, in particular for documentation updates, and conducting empirical studies to enhance comprehension of the needs of documentation users.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 4","pages":"1334-1357"},"PeriodicalIF":6.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661524","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}

{"title":"Trusting Code in the Wild: Exploring Contributor Reputation Measures to Review Dependencies in the Rust Ecosystem","authors":"Sivana Hamer;Nasif Imtiaz;Mahzabin Tamanna;Preya Shabrina;Laurie Williams","doi":"10.1109/TSE.2025.3551664","DOIUrl":"10.1109/TSE.2025.3551664","url":null,"abstract":"Developers rely on open-source packages and must review dependencies to safeguard against vulnerable or malicious upstream code. A careful review of all dependencies changes often does not occur in practice. Therefore, developers need signals to inform of dependency changes that require additional examination, particularly measures for contributor reputation. The goal of this study is to help developers prioritize dependency review efforts by analyzing contributor reputation measures as a signal in the Rust ecosystem. We use network centrality measures to proxy contributor reputation using collaboration activity. We employ a mixed method methodology from the top 1,644 packages in the Rust ecosystem to build a network of 6,949 developers, survey 285 developers, and model 5 centrality measures. Through our survey, we find that only 24% of respondents often review dependencies before adding or updating a package, mentioning difficulties in the review process and signals are therefore employed. Particularly, 51% of respondents often consider contributor reputation when reviewing dependencies. We further explore contributor reputation through network centrality measures employing multivariate mixed-effect linear regression models. We find that the closeness centrality measure is a significant factor in explaining how developers choose to review dependencies. Yet, centrality measures alone do not account for how developers choose to review dependencies. We recommend the Rust ecosystem implement a contributor reputation badge based on our modeled coefficients to complement developers’ dependency review efforts.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 4","pages":"1319-1333"},"PeriodicalIF":6.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661526","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}

{"title":"When Crypto Fails: Demystifying Cryptographic Defects in Ethereum Smart Contracts","authors":"Jiashuo Zhang;Jiachi Chen;Yiming Shen;Tao Zhang;Yanlin Wang;Ting Chen;Jianbo Gao;Zhong Chen","doi":"10.1109/TSE.2025.3551776","DOIUrl":"10.1109/TSE.2025.3551776","url":null,"abstract":"Ethereum has officially provided a set of system-level cryptographic APIs to enhance smart contracts with cryptographic capabilities. These APIs have been utilized in over 13.8% of Ethereum transactions, motivating developers to implement various on-chain cryptographic tasks, such as digital signatures. However, since developers may not always be cryptographic experts, their ad-hoc and potentially defective implementations could compromise the theoretical guarantees of cryptography, leading to real-world security issues. To mitigate this threat, we conducted a comprehensive study aimed at demystifying and detecting cryptographic defects in smart contracts. Through the analysis of 3,762 real-world security reports, we defined 12 types of cryptographic defects in smart contracts with detailed descriptions and practical detection patterns. Based on this categorization, we proposed <sc>CryptoScan</small>, the first static analyzer to automate the pre-deployment detection of cryptographic defects in smart contracts. <sc>CryptoScan</small> utilizes cross-contract and inter-procedure static analysis to identify crypto-related execution paths and employs taint analysis to extract fine-grained crypto-specific semantics for defect detection. Furthermore, we collected a large-scale dataset containing 79,598 real-world crypto-related smart contracts and evaluated <sc>CryptoScan</small>'s effectiveness on it. The results demonstrated that <sc>CryptoScan</small> achieves an overall precision of 96.1% and a recall of 93.3%. Notably, <sc>CryptoScan</small> revealed that 19,707 (24.8%) out of 79,598 smart contracts contain at least one cryptographic defect. Although not all defects directly cause financial losses, they indicate prevalent non-standard cryptographic implementations that should be addressed in real-world practices.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 5","pages":"1381-1398"},"PeriodicalIF":6.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661527","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}

{"title":"Evaluating Spectrum-Based Fault Localization on Deep Learning Libraries","authors":"Ming Yan;Junjie Chen;Tianjie Jiang;Jiajun Jiang;Zan Wang","doi":"10.1109/TSE.2025.3552622","DOIUrl":"10.1109/TSE.2025.3552622","url":null,"abstract":"Deep learning (DL) libraries have become increasingly popular and their quality assurance is also gaining significant attention. Although many fault detection techniques have been proposed, effective fault localization techniques tailored to DL libraries are scarce. Due to the unique characteristics of DL libraries (e.g., complicated code architecture supporting DL model training and inference with extensive multidimensional tensor calculations), the effectiveness of existing fault localization techniques for traditional software is also unknown on DL library faults. To bridge this gap, we conducted the first empirical study to investigate the effectiveness of fault localization on DL libraries. Specifically, we evaluated spectrum-based fault localization (SBFL) due to its high generalizability and affordable overhead on such complicated libraries. Based on the key aspects in SBFL, our study investigated the effectiveness of SBFL with different sources of passing test cases (including human-written, fuzzer-generated, and mutation-based test cases) and various suspicious value calculation methods. In particular, mutation-based test cases are produced by our designed rule-based mutation technique and LLM-based mutation technique tailored to DL library faults. To enable our extensive study, we built the first benchmark (Defects4DLL), which contains 120 real-world faults in PyTorch and TensorFlow with easy-to-use experimental environments. Our study delivered a series of useful findings. For example, the rule-based approach is effective in localizing crash faults in DL libraries, successfully localizing 44.44% of crash faults within Top-10 functions and 74.07% of crash faults within Top-10 files, while the passing test cases from DL library fuzzers perform poorly on this task. Furthermore, based on our findings on the complementarity of different sources, we designed a hybrid technique by effectively integrating human-written, LLM-mutated, rule-based mutated test cases, which further achieves 31.48%<inline-formula><tex-math>$boldsymbol{sim}$</tex-math></inline-formula>61.36% improvements over each single source in terms of the number of detected faults within Top-5 files.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 5","pages":"1399-1414"},"PeriodicalIF":6.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661511","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}

{"title":"50 Years of Transactions on Software Engineering","authors":"Sebastian Uchitel","doi":"10.1109/tse.2025.3540338","DOIUrl":"https://doi.org/10.1109/tse.2025.3540338","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"19 1","pages":"663-665"},"PeriodicalIF":7.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640482","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}

{"title":"Using Cooperative Co-Evolutionary Search to Generate Metamorphic Test Cases for Autonomous Driving Systems","authors":"Hossein Yousefizadeh;Shenghui Gu;Lionel C. Briand;Ali Nasr","doi":"10.1109/TSE.2025.3570897","DOIUrl":"10.1109/TSE.2025.3570897","url":null,"abstract":"Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios highlights the need for rigorous system-level testing to ensure safety, a task usually performed with a simulator in the loop. Though one important goal of such testing is to detect safety violations, there are many undesirable system behaviors, that may not immediately lead to violations, that testing should also be focusing on, thus detecting more subtle problems and enabling a finer-grained analysis. This paper introduces Cooperative Co-evolutionary MEtamorphic test Generator for Autonomous systems (CoCoMEGA), a novel automated testing framework aimed at advancing system-level safety assessments of ADSs. CoCoMEGA combines Metamorphic Testing (MT) with a search-based approach utilizing Cooperative Co-Evolutionary Algorithms (CCEA) to efficiently generate a diverse set of test cases. CoCoMEGA emphasizes the identification of test scenarios that present undesirable system behavior, that may eventually lead to safety violations, captured by Metamorphic Relations (MRs). When evaluated within the CARLA simulation environment on the Interfuser ADS, CoCoMEGA consistently outperforms baseline methods, demonstrating enhanced effectiveness and efficiency in generating severe, diverse MR violations and achieving broader exploration of the test space. Further expert assessments of these violations confirmed that most represent real safety risks, which validates their practical relevance. These results underscore CoCoMEGA as a promising, more scalable solution to the inherent challenges in ADS testing with a simulator in the loop. Future research directions may include extending the approach to additional simulation platforms, applying it to other complex systems, and exploring methods for further improving testing efficiency such as surrogate modeling.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 6","pages":"1882-1911"},"PeriodicalIF":6.5,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066048","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}

{"title":"Enhanced Smart Contract Vulnerability Detection via Graph Neural Networks: Achieving High Accuracy and Efficiency","authors":"Chang Xu;Huaiyu Xu;Liehuang Zhu;Xiaodong Shen;Kashif Sharif","doi":"10.1109/TSE.2025.3570421","DOIUrl":"10.1109/TSE.2025.3570421","url":null,"abstract":"As blockchain technology becomes prevalent, smart contracts have shown significant utility in finance and supply chain management. However, vulnerabilities in smart contracts pose serious threats to blockchain security, leading to substantial economic losses. Therefore, developing effective vulnerability detection solutions is urgent. To address this issue, we propose a method for detecting vulnerabilities in smart contracts using graph neural networks (GNNs) that can identify eight common vulnerabilities. Our method is fully automated, applicable to all Ethereum smart contracts, and does not require expert-defined rules or manually defined features. We extract the Control Flow Graph and Abstract Syntax Graph from the smart contract code, which are then processed by a GNN to generate feature vectors for classification. Experiments on a real Ethereum dataset demonstrate that our method significantly outperforms existing state-of-the-art approaches. For individual detection tasks, the combined source code and bytecode method achieves an average accuracy of 95.78%, with a peak of 99.13%, and an average F1 score of 93.80%. Compared to competitors, our method shows an average improvement of 51.92% in accuracy and 47.21% in F1 score. The bytecode-only method achieves an average accuracy of 94.68% and an F1 score of 92.36%. For multi-class tasks, both methods achieve high accuracies of 91.26% and 87.34%, with F1 scores of 97.42% and 96.43%, respectively.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 6","pages":"1854-1865"},"PeriodicalIF":6.5,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066087","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}

{"title":"Anchor Attention, Small Cache: Code Generation With Large Language Models","authors":"Xiangyu Zhang;Yu Zhou;Guang Yang;Harald C. Gall;Taolue Chen","doi":"10.1109/TSE.2025.3570680","DOIUrl":"10.1109/TSE.2025.3570680","url":null,"abstract":"The development of large language models (LLMs) has revolutionized automated code generation. However, their high demand of computation resources has hindered a broader deployment and raised environmental concerns. A common strategy for diminishing computational demands is to cache Key-Value (KV) states from the attention mechanism which is adopted predominately by mainstream LLMs. It can mitigate the need of repeated attention computations, but brings significant memory overhead. Current practices in NLP often use sparse attention which may, unfortunately, lead to substantial inaccuracies, or hallucinations, in code generation tasks. In this paper, we analyze the attention weights distribution within code generation models via an empirical study, uncovering a sparsity pattern, i.e., the aggregation of information at specific anchor points. Based on this observation, we propose a novel approach, <monospace>AnchorCoder</monospace>, which features token-wise anchor attention designed to extract and compress the contextual information, and layer-wise anchor attention enabling cross-layer communication to mitigate the issue of excessive superposition caused by the compression. The extensive experiments across multiple benchmark datasets confirm the effectiveness of <monospace>AnchorCoder</monospace>, which can consistently achieve a significant (at least 70%) reduction in KV cache requirements, while preserving the majority of model’s performance.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 6","pages":"1866-1881"},"PeriodicalIF":6.5,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066086","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}

{"title":"Isolating Compiler Faults Through Differentiated Compilation Configurations","authors":"Yibiao Yang;Qingyang Li;Maolin Sun;Jing Yang;Jiangchang Wu;Yuming Zhou","doi":"10.1109/TSE.2025.3569530","DOIUrl":"10.1109/TSE.2025.3569530","url":null,"abstract":"Compilation optimization bugs are prevalent and can significantly affect the correctness of software products, posing serious challenges to software development. Identifying and localizing these bugs are critical tasks for compiler developers. However, the intricate nature and extensive scale of modern compilers make it difficult to pinpointing the root causes of such bugs. Previous research has introduced innovative techniques that generate <italic>witness test programs</i>–tests that pass–by mutating bug-triggering test cases, highlighting the importance of this problem and demonstrating the effectiveness of such approaches. Nevertheless, existing techniques based on witness test programs generation suffer from inherent limitations. Specifically, they do not guarantee the successful creation of witness test programs via mutation and are often time-consuming, typically requiring extensive iterations to produce a valid witness test program. In this study, we present <sc>Odfl</small>, a simple yet effective approach for automatically isolating compiler optimization faults by introducing the concept of <italic>differentiated compilation configurations</i>. The core insight behind <sc>Odfl</small> is that modifying compilation settings such as disabling fine-grained compilation flags in GCC or reducing the number of fine-grained compilation passes in LLVM, can suppress the manifestation of compiler bugs triggered by the same test program. Through adjusting these settings, <sc>Odfl</small> creates differentiated compilation configuration that produce multiple compiler executions with distinct pass/fail outcomes. We utilize these differentiated configurations to collect both passing and failing compiler coverage, and then apply <italic>Spectrum-Based Fault Localization (SBFL)</i> techniques to rank compiler source files based on their suspiciousness. Our evaluation of 60 GCC and 50 LLVM compiler bugs demonstrates that <sc>Odfl</small> substantially outperforms state-of-the-art compiler fault localization techniques in terms of both effectiveness and efficiency. Notably, <sc>Odfl</small> achieves over 90% improvement in accurately ranking the top-1 faulty source files compared to three existing techniques–DiWi, RecBi, and LLM4CBI–and reduces fault localization time by more than 99% on average.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 6","pages":"1838-1853"},"PeriodicalIF":6.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946317","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}