@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://aclanthology.org/2025.acl-long.426/",
doi = "10.18653/v1/2025.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}."
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<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 https://github.com/gersteinlab/LocAgent.</abstract>
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%0 Conference Proceedings
%T LocAgent: Graph-Guided LLM Agents for Code Localization
%A Chen, Zhaoling
%A Tang, Robert
%A Deng, Gangda
%A Wu, Fang
%A Wu, Jialong
%A Jiang, Zhiwei
%A Prasanna, Viktor
%A Cohan, Arman
%A Wang, Xingyao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F chen-etal-2025-locagent
%X 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 https://github.com/gersteinlab/LocAgent.
%R 10.18653/v1/2025.acl-long.426
%U https://aclanthology.org/2025.acl-long.426/
%U https://doi.org/10.18653/v1/2025.acl-long.426
%P 8697-8727
Markdown (Informal)
[LocAgent: Graph-Guided LLM Agents for Code Localization](https://aclanthology.org/2025.acl-long.426/) (Chen et al., ACL 2025)
ACL
- Zhaoling Chen, Robert Tang, Gangda Deng, Fang Wu, Jialong Wu, Zhiwei Jiang, Viktor Prasanna, Arman Cohan, and Xingyao Wang. 2025. LocAgent: Graph-Guided LLM Agents for Code Localization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8697–8727, Vienna, Austria. Association for Computational Linguistics.