Improved IR-based Bug Localization with Intelligent Relevance Feedback
This program is tentative and subject to change.
Software bugs pose a significant challenge during development and maintenance, and practitioners spend nearly 50% of their time dealing with bugs. Many existing techniques adopt Information Retrieval (IR) to localize a reported bug using textual and semantic relevance between bug reports and source code. However, they often struggle to bridge a critical gap between bug reports and code that requires in-depth contextual understanding, which goes beyond textual or semantic relevance. In this paper, we present a novel technique for bug localization –BRaIn– that addresses the contextual gaps by assessing the relevance between bug reports and code with Large Language Models (LLM). It then leverages the LLM’s feedback (a.k.a., Intelligent Relevance Feedback) to reformulate queries and re-rank source documents, improving bug localization. We evaluate BRaIn using a benchmark dataset –Bench4BL– and three performance metrics and compare it against six baseline techniques from the literature. Our experimental results show that BRaIn outperforms baselines by 87.6%, 89.5%, and 48.8% margins in MAP, MRR, and HIT@K, respectively. Additionally, it can localize ~52% of bugs that cannot be localized by the baseline techniques due to the poor quality of corresponding bug reports. By addressing the contextual gaps and introducing Intelligent Relevance Feedback, BRaIn advances not only theory but also improves the IR-based bug localization.
This program is tentative and subject to change.
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 | ||
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 | ||
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 | ||
16:50 10mTalk | 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, Canada, Kevin Schneider University of Saskatchewan, Masud Rahman Dalhousie University, Kartik Mittal University of Saskatchewan, Ryder Hardy University of Saskatchewan | ||
17:00 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 | ||
17:10 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 | ||
17:20 10mLive Q&A | Session's Discussion: "Log Parsing, Bug Localisation, Review Comprehension" Research Track | ||