Towards Enhancing IR-based Bug Localization Leveraging Texts and Multimedia from Bug Reports
This program is tentative and subject to change.
Software bug reports often miss critical information, delaying their resolution. The last decade has seen a growing trend of combining textual and multimedia information from software artifacts (e.g., bug reports, and programming questions) to support various software engineering tasks (e.g., duplicate bug report detection, and bug reproduction). However, none of the studies performs a fine-grained analysis of the multimedia information attached to bug reports. Hence, it is not clear what their attached images or videos contain or whether they could help identify software bugs or errors automatically. In this paper, we conduct a preliminary study that investigates the presence or prevalence of key elements in 1,469 images attached to 1,000 bug reports and demonstrate their potential to support IR-based bug localization. We have several interesting findings. First, our analysis suggests that the attached images to visual bug reports contain a mix of UI components, programming components, and regular texts. Second, our analysis using an LLM (e.g., GPT4o) suggests that it can extract the key elements from the attached images effectively, posing a suitable alternative to human annotators. Finally, our experiments suggest that the multimedia information extracted from the attached images can enhance the performance of a traditional technique for bug localization by improving 34.06% of its search queries.
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 | ||