# LLM-Based Agents for Identifying Bug-Introducing Commits

arXiv: https://arxiv.org/abs/2603.29378 · HTML: https://mlsanity.com/paper/2603.29378/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 8 of 8)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution](https://mlsanity.com/paper/2401.03065/) (2024)
- [Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection](https://mlsanity.com/paper/2306.17193/) (2023)
- [Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection](https://mlsanity.com/paper/2408.12986/) (2024)
- [Evaluating SZZ Implementations Through a Developer-informed Oracle](https://mlsanity.com/paper/2102.03300/) (2021)
- [Vulnerability-Affected Versions Identification: How Far Are We?](https://mlsanity.com/paper/2509.03876/) (2025)

## Built on by (most cited first, 1 of 1)

- [ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents](https://mlsanity.com/paper/2605.14153/) (2026)

## Abstract (arXiv)

'Sliwerski, Zimmermann, and Zeller (SZZ) just won the 2026 ACM SIGSOFT Impact Award for asking: When do changes induce fixes? Their paper from 2005 served as the foundation for a wide array of approaches aimed at identifying bug-introducing changes (or commits) from fix commits in software repositories. But even after two decades of progress, the best-performing approach from 2025 yields a modest increase of 10 percentage points in F1-score on the most popular Linux kernel dataset. In this paper, we uncover how and why LLM-based agents can substantially advance the state-of-the-art in identifying bug-introducing commits from fix commits. We propose a simple agentic workflow based on searching a set of candidate commits and find that it raises the F1-score from 0.64 to 0.81 on the most popular Linux kernel dataset, a bigger jump than between the original 2005 method (0.54) and the previous SOTA (0.64). We also uncover why agents are so successful: They derive short greppable patterns from the fix commit diff and message and use them to effectively search and find bug-introducing commits in large candidate sets. Finally, we also discuss how these insights might enable further progress in root cause understanding and repair.