Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks

• URL: http://arxiv.org/abs/2505.00990v1
• Date: Fri, 02 May 2025 04:29:09 GMT
• Title: Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks
• Authors: Jiaqi Zhang, Shikai Guo, Hui Li, Chenchen Li, Yu Chai, Rong Chen,
• Abstract summary: We propose a method called RC-Detection to detect root-cause deletion lines in changed code lines.<n>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.<n>Our experiments 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.
• Score: 7.676213873923721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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