@inproceedings{chen-etal-2025-locagent,
    title = "{L}oc{A}gent: Graph-Guided {LLM} Agents for Code Localization",
    author = "Chen, Zhaoling  and
      Tang, Robert  and
      Deng, Gangda  and
      Wu, Fang  and
      Wu, Jialong  and
      Jiang, Zhiwei  and
      Prasanna, Viktor  and
      Cohan, Arman  and
      Wang, Xingyao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.426/",
    pages = "8697--8727",
    ISBN = "979-8-89176-251-0",
    abstract = "Code localization{--}identifying precisely where in a codebase changes need to be made{--}is a fundamental yet challenging task in software maintenance. Existing approaches struggle to efficiently navigate complex codebases when identifying relevant code snippets.The challenge lies in bridging natural language problem descriptions with the target code elements, often requiring reasoning across hierarchical structures and multiple dependencies.We introduce LocAgent, a framework that addresses code localization through a graph-guided agent.By parsing codebases into directed heterogeneous graphs, LocAgent creates a lightweight representation that captures code structures and their dependencies, enabling LLM agents to effectively search and locate relevant entities through powerful multi-hop reasoning.Experimental results on real-world benchmarks demonstrate that our approach significantly enhances accuracy in code localization.Notably, our method with the fine-tuned Qwen-2.5-Coder-Instruct-32B model achieves comparable results to SOTA proprietary models at greatly reduced cost (approximately 86{\%} reduction), reaching up to 92.7{\%} accuracy on file-level localization while improving downstream GitHub issue resolution success rates by 12{\%} for multiple attempts (Pass@10). Our code is available at \url{https://github.com/gersteinlab/LocAgent}."
}