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File size: 2,487 Bytes
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---
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
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- name: patch
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- name: test_patch
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- name: problem_statement
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- name: hints_text
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- name: created_at
dtype: int64
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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
<!-- Provide a quick summary of the dataset. -->
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}
}
``` |