Context: Contemporary code review tools are a popular choice for software quality assurance. Using these tools, reviewers are able to post a linkage between two patches during a review discussion. Large development teams that use a review-then-commit model risk being unaware of these linkages. Objective: Our objective is to first explore how patch linkage impacts the review process. We then propose and evaluate models that detect patch linkage based on realistic time intervals. Method: First, we carry out an exploratory study on three open source projects to conduct linkage impact analysis using 942 manually classified linkages. Second, we propose two techniques using textual and file location similarity to build detection models and evaluate their performance. Results: The study provides evidence of latency in the linkage notification. We show that a patch with the Alternative Solution linkage (i.e., patches that implement similar functionality)undergoes a quicker review and avoids additional revisions after the team has been notified, compared to other linkage types. Our detection model experiments show promising recall rates for the Alternative Solution linkage (from 32% to 95%), but precision has room for improvement. Conclusion: Patch linkage detection is promising, with likely improvements if the practice of posting linkages becomes more prevalent. From our implications, this paper lays the groundwork for future research on how to increase patch linkage awareness to facilitate efficient reviews.
Automated Detection of Configuration-Specific Security Vulnerabilities via Patch Analysis
Federal University of Bahia（巴伊亚联邦大学）；University of Notre Dame（圣母大学）；Federal Rural University of Pernambuco（伯南布哥联邦农村大学）；Federal University of Rio de Janeiro（里约热内卢联邦大学）
Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios
Centre for Research on Intelligent Software Engineering（智能软件工程研究中心）；School of Computing and Information Systems（计算与信息系统学院）；Singapore Management University（新加坡管理大学）
Empirical Study of Code Large Language Models for Binary Security Patch Detection
National Key Laboratory for Novel Software Technology at Nanjing University（南京大学新型软件技术国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科学大学）
