Detecting the Root Cause Code Lines in Bug-Fixing Commits by Heterogeneous Graph Learning

2025-05-02

AI Summary

Existing approaches struggle to localize root causes of bug-fixing commits in complex software due to their inability to model heterogeneous commit structures and cross-line code dependencies, limiting localization accuracy. To address this, we propose a root-cause localization method based on heterogeneous graph neural networks. Our approach constructs a multi-granularity program dependency heterogeneous graph integrating Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs). We design a cross-line semantic preservation mechanism that modulates semantic propagation via a decay-enhancement gating scheme, and introduce a heterogeneous attention-driven semantic aggregation module to explicitly capture fine-grained dependencies among diverse node and edge types. Evaluated on 675 bug-fixing commits across 87 open-source projects, our method achieves an average 73.4% improvement in the MFR metric over state-of-the-art baselines, with a maximum gain of 96.83%, significantly enhancing the precision of identifying critical buggy lines.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

With the continuous growth in the scale and complexity of software systems, defect remediation has become increasingly difficult and costly. Automated defect prediction tools can proactively identify software changes prone to defects within software projects, thereby enhancing software development efficiency. However, existing work in heterogeneous and complex software projects continues to face challenges, such as struggling with heterogeneous commit structures and ignoring cross-line dependencies in code changes, which ultimately reduce the accuracy of defect identification. To address these challenges, we propose an approach called RC_Detector. RC_Detector comprises three main components: the bug-fixing graph construction component, the code semantic aggregation component, and the cross-line semantic retention component. The bug-fixing graph construction component identifies the code syntax structures and program dependencies within bug-fixing commits and transforms them into heterogeneous graph formats by converting the source code into vector representations. The code semantic aggregation component adapts to heterogeneous data by using heterogeneous attention to learn the hidden semantic representation of target code lines. The cross-line semantic retention component regulates propagated semantic information by using attenuation and reinforcement gates derived from old and new code semantic representations, effectively preserving cross-line semantic relationships. Extensive experiments were conducted to evaluate the performance of our model by collecting data from 87 open-source projects, including 675 bug-fixing commits. The experimental results demonstrate that our model outperforms state-of-the-art approaches, achieving significant improvements of 83.15%,96.83%,78.71%,74.15%,54.14%,91.66%,91.66%, and 34.82% in MFR, respectively, compared with the state-of-the-art approaches.

Problem

• Detecting root cause code lines in bug-fixing commits
• Handling heterogeneous commit structures in software projects
• Preserving cross-line dependencies for accurate defect identification

Innovation

• Heterogeneous graph learning for bug detection
• Semantic aggregation with heterogeneous attention
• Cross-line semantic retention via attenuation gates