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This article introduces the latest research on using LLM-based Agents to identify defect-introducing commits in software. This method increased the F1 score from 0.64 to 0.81 on the Linux kernel dataset, surpassing the incremental improvements over the past 20 years.
Section 01
This article introduces a study on using LLM-based Agents to identify defect-introducing commits in software. This method increased the F1 score from 0.64 to 0.81 on the Linux kernel dataset, surpassing the incremental improvements based on the SZZ algorithm over the past 20 years, marking a paradigm shift in this field.
Section 02
In 2005, Śliwerski, Zimmermann, and Zeller proposed the SZZ algorithm, laying the foundation for software defect localization (winning the 2026 ACM SIGSOFT Impact Paper Award). However, over the past two decades, despite numerous improvements, the F1 score on the Linux kernel dataset only increased from 0.54 to 0.64, suggesting that traditional heuristic rules and static analysis methods may have reached their limits.
Section 03
The latest research proposes a new method based on LLM Agents, redefining defect-introducing commit identification as an agent search problem. The agent actively explores the candidate commit space, uses the reasoning ability of language models to make dynamic decisions, and achieves an F1 score of 0.81 on the Linux kernel dataset, surpassing all previous progress.
Section 04
The core of the agent's success lies in extracting concise searchable patterns (greppable patterns) from the code differences (diff) and commit information of the fix commit. The steps include: analyzing the fix commit to understand the nature of the defect → generating search patterns such as specific code snippets/function calls → using the patterns to efficiently search candidate commits to locate the defect-introducing commit. These patterns can be dynamically adjusted to adapt to different programming styles and structures.
Section 05
Traditional SZZ and its variants rely on static analysis to track the origin of code lines, but face limitations such as line number changes due to code refactoring, complex multi-file associations, and inability to semantically understand the intent of changes. LLM Agents use semantic understanding capabilities to grasp the intent of changes beyond grammatical constraints, leading to a significant improvement in F1 score.
Section 06
This research has practical significance for software enterprises (improving code review efficiency) and open-source communities (assisting maintainers in understanding defect propagation). It also reveals a general methodology for agents in software engineering: generating concise search patterns + goal-oriented exploration, pointing the way for fields such as defect detection, root cause analysis, and automatic repair.
Section 07
From SZZ to LLM Agents, the field of software defect localization is undergoing a paradigm shift. Two decades of incremental improvements have been surpassed by a single innovation, indicating that agents will play a transformative role in more areas of software engineering. As LLM capabilities improve, we look forward to more breakthroughs in code understanding, defect prevention, and automatic repair.
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