{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/identifying-root-cause-of-bugs-by-capturing","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","arxiv_id":"2505.00990","date":"2025-05-02","proceeding":null,"authors":["Jiaqi Zhang","Shikai Guo","Hui Li","Chenchen Li","Yu Chai","Rong Chen"],"abstract":"The Just-In-Time defect prediction model helps development teams improve software quality and efficiency by assessing whether code changes submitted by developers are likely to introduce defects in real-time, allowing timely identification of potential issues during the commit stage. However, two main challenges exist in current work due to the reality that all deleted and added lines in bug-fixing commits may be related to the root cause of the introduced bug: 1) lack of effective integration of heterogeneous graph information, and 2) lack of semantic relationships between changed code lines. To address these challenges, we propose a method called RC-Detection, which utilizes relational graph convolutional network to capture the semantic relationships between changed code lines. RC-Detection is used to detect root-cause deletion lines in changed code lines, thereby identifying the root cause of introduced bugs in bug-fixing commits. To evaluate the effectiveness of RC-Detection, we used three datasets that contain high-quality bug-fixing and bug-introducing commits. Extensive experiments were conducted to evaluate the performance of our model by collecting data from 87 open-source projects, including 675 bug-fix commits. The experimental results show that, compared to the most advanced root cause detection methods, RC-Detection improved Recall@1, Recall@2, Recall@3, and MFR by at 4.107%, 5.113%, 4.289%, and 24.536%, respectively.","url_abs":"https://arxiv.org/abs/2505.00990v1","url_pdf":"https://arxiv.org/pdf/2505.00990v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"identifying-root-cause-of-bugs-by-capturing","repo_url":"https://github.com/leling666/rc-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}