metadata

license: apache-2.0
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
  splits:
    - name: test
      num_bytes: 9540894
      num_examples: 660
  download_size: 3490581
  dataset_size: 9540894

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 for evaluating code localization methods and for easy comparison with our approach.

Code: https://github.com/gersteinlab/LocAgent

📊 Details

This is the dataset that was used in the early version of our paper. We later released a refined version, czlll/Loc-Bench_V1, with improved data quality by filtering out examples that do not modify any functions. We recommend using the refined dataset to evaluate code localization performance.

The table below shows the distribution of categories in the dataset Loc-Bench_V0.1.

category	count
Bug Report	282
Feature Request	203
Performance Issue	144
Security Vulnerability	31

🔧 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_V0.1", 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}
}