metadata

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
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: test
      num_bytes: 8489578
      num_examples: 560
  download_size: 3084430
  dataset_size: 8489578
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

LOC-BENCH: A Benchmark for Code Localization

LOC-BENCH is a dataset specifically designed to evaluate code localization methods in software repositories. LOC-BENCH provides diverse issues, including bug reports, feature requests, security vulnerabilities, and performance optimizations.

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

📊 Details

Loc-Bench_V1 is our official dataset for comparison with our approach.

The table below shows the distribution of categories in the dataset.

category	count
Bug Report	242
Feature Request	150
Performance Issue	139
Security Vulnerability	29

Previous Versions

Loc-Bench_V0.1: The dataset used in the early version of our paper. Some examples in this dataset do not involve function-level code modifications but instead focus on modifying classes. V1 filters out 100 examples without function-level code modifications, creating a cleaner subset of the dataset.
Loc-Bench_V0.2: Filtering out examples that do not involve function-level code modifications and then supplementing the dataset to restore it to the original size of 660 examples.

🔧 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_V1", 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}
}