metadata

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

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

📊 Details

Compared to the V0, 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}
}