Reformulate, Retrieve, Localize: Agents for Repository-Level Bug Localization
This addresses the time-consuming challenge of bug localization for software developers, representing an incremental improvement over existing methods.
The study tackled the problem of bug localization in large software repositories by using an LLM-powered agent for query reformulation and retrieval, achieving a 35% improvement in first-file retrieval ranking over a BM25 baseline and up to 22% better file retrieval performance compared to SWE-agent.
Bug localization remains a critical yet time-consuming challenge in large-scale software repositories. Traditional information retrieval-based bug localization (IRBL) methods rely on unchanged bug descriptions, which often contain noisy information, leading to poor retrieval accuracy. Recent advances in large language models (LLMs) have improved bug localization through query reformulation, yet the effect on agent performance remains unexplored. In this study, we investigate how an LLM-powered agent can improve file-level bug localization via lightweight query reformulation and summarization. We first employ an open-source, non-fine-tuned LLM to extract key information from bug reports, such as identifiers and code snippets, and reformulate queries pre-retrieval. Our agent then orchestrates BM25 retrieval using these preprocessed queries, automating localization workflow at scale. Using the best-performing query reformulation technique, our agent achieves 35% better ranking in first-file retrieval than our BM25 baseline and up to +22% file retrieval performance over SWE-agent.