--- license: apache-2.0 task_categories: - text-generation configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: repo dtype: string - name: instance_id dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: int64 - name: labels sequence: string - name: category dtype: string - name: edit_functions sequence: string - name: added_functions sequence: string - name: edit_functions_length dtype: int64 splits: - name: test num_bytes: 11007786 num_examples: 660 download_size: 3913633 dataset_size: 11007786 --- # LOC-BENCH: A Benchmark for Code Localization LOC-BENCH is a dataset specifically designed for evaluating code localization methods in software repositories. LOC-BENCH provides a diverse set of issues, including bug reports, feature requests, security vulnerabilities, and performance optimizations. Please refer to the official version [**`Loc-Bench_V1`**](https://huggingface.co/datasets/czlll/Loc-Bench_V1) for evaluating code localization methods and for easy comparison with our approach. Code: https://github.com/gersteinlab/LocAgent ## 📊 Details Compared to the [V0](https://huggingface.co/datasets/czlll/Loc-Bench_V0.1), it filters out examples that do not involve function-level code modifications and then supplements the dataset to restore it to the original size of 660 examples. The table below shows the distribution of categories in the dataset. | category | count | |:---------|:---------| | Bug Report | 275 | | Feature Request | 216 | | Performance Issue | 140 | | Security Vulnerability | 29 | ## 🔧 How to Use You can easily load LOC-BENCH using Hugging Face's datasets library: ``` from datasets import load_dataset dataset = load_dataset("czlll/Loc-Bench_V0.2", split="test") ``` ## 📄 Citation If you use LOC-BENCH in your research, please cite our paper: ``` @article{chen2025locagent, title={LocAgent: Graph-Guided LLM Agents for Code Localization}, author={Chen, Zhaoling and Tang,Xiangru and Deng,Gangda and Wu,Fang and Wu,Jialong and Jiang,Zhiwei and Prasanna,Viktor and Cohan,Arman and Wang,Xingyao}, journal={arXiv preprint arXiv:2503.09089}, year={2025} } ```