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[ACL 2025] Graph-guided agentic framework for code localization https://arxiv.org/abs/2503.09089
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| 2026-08-06 | 624 |
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# LocAgent: Graph-Guided LLM Agents for Code Localization <p align="center"> 📑 <a href="https://arxiv.org/abs/2503.09089" target="_blank">Paper</a> | 📊 <a href="https://huggingface.co/datasets/czlll/Loc-Bench_V1" target="_blank">Loc-bench</a> | 🤗 <a href="https://huggingface.co/czlll/Qwen2.5-Coder-7B-CL" target="_blank">Qwen2.5-Coder-7B-CL</a> | 🤗 <a href="https://huggingface.co/czlll/Qwen2.5-Coder-32B-CL" target="_blank">Qwen2.5-Coder-32B-CL</a> </p> ## ℹ️ Overview We introduce **LocAgent**, a framework that addresses code localization through graph-based representation. 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. <!-- <div align="center"> <img src="./assets/overview.png" alt="Overview" width="800"> </div> -->  ## ⚙️ Setup 1. Follow these steps to set up your development environment: ``` git clone [email protected]:gersteinlab/LocAgent.git cd LocAgent conda create -n locagent python=3.12 conda activate locagent pip install -r requirements.txt ``` ## 🚀 Launch LocAgent 1. (Optional but recommended) Parse the codebase for each issue in the benchmark to generate graph indexes in batch. ``` python dependency_graph/batch_build_graph.py \ --dataset 'czlll/Loc-Bench_V1' \ --split 'test' \ --num_processes 50 \ --download_repo ``` - `dataset`: select the benchmark (by default it will be `SWE-Bench_Lite`); you can choose from `['czlll/SWE-bench_Lite', 'czlll/Loc-Bench_V1']`(adapted for code localization) and SWE-bench series datasets like `['princeton-nlp/SWE-bench_Lite', 'princeton-nlp/SWE-bench_Verified', 'princeton-nlp/SWE-bench']` - `repo_path`: the directory where you plan to pull or have already pulled the codebase - `index_dir`: the base directory where the generated graph index will be saved - `download_repo`: whether to download the codebase to `repo_path` before indexing 2. Export the directory of the graph indexes and the BM25 sparse index. If not generated in advance, the graph index will be generated during the localization process. ``` export GRAPH_INDEX_DIR='{INDEX_DIR}/{DATASET_NAME}/graph_index_v2.3' export BM25_INDEX_DIR='{INDEX_DIR}/{DATASET_NAME}/BM25_index' ``` 2. Run the script `scripts/run_lite.sh` to lauch LocAgent. ``` python auto_search_main.py \ --dataset 'czlll/SWE-bench_Lite' \ --split 'test' \ --model 'azure/gpt-4o' \ --localize \ --merge \ --output_folder $result_path/location \ --eval_n_limit 300 \ --num_processes 50 \ --use_function_calling \ --simple_desc ``` - `localize`: set to start the localization process - `merge`: merge the result of multiple samples - `use_function_calling`: enable function calling features of LLMs. If disabled, codeact will be used to support function calling - `simple_desc`: use simplified function descriptions due to certain LLM limitations. Set to False for better performance when using Claude. 3. Evaluation After localization, the results will be saved in a JSONL file. You can evaluate them using `evaluation.eval_metric.evaluate_results`. Refer to `evaluation/run_evaluation.ipynb` for a demonstration. ## 📑 Cite Us ``` @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", b
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Robert · @gersteinlab · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b87e351725e0ac81, desc:agentic