Building Bridges, Not Walls: Fairness-aware and Accurate Recommendation of Code Reviewers via LLM-based Agents Collaboration
Code review is essential for maintenance of pull request-based software systems. Recommending suitable reviewers for code changes can facilitate defect detection and knowledge dissemination. Despite extensive research, the inherent complexity of pull requests (PRs) and reviewer profiles continues to cause challenge for accurate matching them together. Furthermore, existing methods often amplify gender and racial/ethnic disparities due to the lack of attention to biases present in historical review records. To address these issues, we first collected a dataset from 4 large-scale open-source projects involving 50-month revision history, reaching up to 30 attributes. This dataset includes gender and racial/ethnic information, which was inferred, validated, and incorporated to enable comprehensive data bias analysis in reviewer recommendation tasks. Additionally, we introduce a fairness-aware and accurate approach: CoReBM, which leverages the advanced semantic understanding capabilities of Large Language Models (LLMs) to comprehensively capture the nuanced textual context of both PRs and reviewers, utilizing the robust planning, collaborative, and decision-making abilities of multi-agent systems. CoReBM integrates diverse factors to improve recommendation performance while mitigating bias effects through the incorporation of candidates’ gender and racial/ethnic attributes. We evaluate the effectiveness of our approach on this dataset, and the results demonstrate that CoReBM outperforms state-of-the-art methods in both accuracy and fairness in recommendation.
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | Log Parsing, Bug Localisation, Review ComprehensionResearch Track / Early Research Achievements (ERA) at 205 Chair(s): Gabriele Bavota Software Institute @ Università della Svizzera Italiana, Coen De Roover Vrije Universiteit Brussel, Gema Rodríguez-Pérez Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus | ||
16:00 10mTalk | Developing a Taxonomy for Advanced Log Parsing Techniques Research Track Issam Sedki Concordia University, Wahab Hamou-Lhadj Concordia University, Montreal, Canada, Otmane Ait-Mohamed Concordia University, Naser Ezzati Jivan | ||
16:10 10mTalk | GELog:A GPT-Enhanced Log Representation Method for Anomaly Detection Research Track Wenwu Xu Institute of Information Engineering, Chinese Academy of Sciences and School of Cyberspace Security, University of Chinese Academy of Sciences, Peng Wang Institute of Information Engineering,Chinese Academy of Sciences, Haichao Shi Institute of Information Engineering,Chinese Academy of Sciences, Guoqiao Zhou Institute of Information Engineering,Chinese Academy of Sciences, Junliang Yao Institute of Information Engineering,Chinese Academy of Sciences, Xiao-Yu Zhang Institute of Information Engineering, Chinese Academy of Science | ||
16:20 10mTalk | Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction Research Track Yifan Wu Peking University, Siyu Yu The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Ying Li School of Software and Microelectronics, Peking University, Beijing, China Pre-print | ||
16:30 10mTalk | LLM-BL: Large Language Models are Zero-Shot Rankers for Bug Localization Research Track Zhengliang Li Nanjing University, Zhiwei Jiang Nanjing University, Qiguo Huang NanJing Audit University, Qing Gu Nanjing University | ||
16:40 10mTalk | Improved IR-based Bug Localization with Intelligent Relevance Feedback Research Track Pre-print | ||
16:50 6mTalk | Towards Enhancing IR-based Bug Localization Leveraging Texts and Multimedia from Bug Reports Early Research Achievements (ERA) Shamima Yeasmin University of Saskatchewan, Chanchal K. Roy University of Saskatchewan, Kevin Schneider University of Saskatchewan, Masud Rahman Dalhousie University, Kartik Mittal University of Saskatchewan, Ryder Hardy University of Saskatchewan Pre-print | ||
16:56 10mTalk | Building Bridges, Not Walls: Fairness-aware and Accurate Recommendation of Code Reviewers via LLM-based Agents Collaboration Research Track Luqiao Wang Xidian University, Qingshan Li Xidian University, Di Cui Xidian University, Mingkang Wang Xidian University, Yutong Zhao University of Central Missouri, Yongye Xu Xidian University, Huiying Zhuang Xidian University, Yangtao Zhou Xidian University, Lu Wang Xidian University | ||
17:06 10mTalk | Code Review Comprehension: Reviewing Strategies Seen Through Code Comprehension Theories Research Track Pavlina Wurzel Goncalves University of Zurich, Pooja Rani University of Zurich, Margaret-Anne Storey University of Victoria, Diomidis Spinellis Athens University of Economics and Business & Delft University of Technology, Alberto Bacchelli University of Zurich Pre-print | ||
17:16 10mTalk | KotSuite: Unit Test Generation for Kotlin Programs in Android Applications Research Track Feng Yang Wuhan University, Qi Xin Wuhan University, Zhilei Ren Dalian University of Technology, Jifeng Xuan Wuhan University | ||
17:26 4mLive Q&A | Session's Discussion: "Log Parsing, Bug Localisation, Review Comprehension" Research Track |