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

TL;DR

This paper introduces RC-Detection, a relational graph neural network-based method that effectively identifies root causes of bugs by capturing semantic relationships between changed code lines, improving defect detection accuracy.

Contribution

The paper presents a novel approach using relational graph convolutional networks to better integrate heterogeneous code change information for root cause analysis.

Findings

RC-Detection outperforms existing methods in recall metrics.

It improves bug root cause detection accuracy significantly.

Experimental results validate the effectiveness across multiple datasets.

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.