# VJBench Dataset

This folder contains vulnerability fix data from the [LLM-VUL dataset](https://github.com/lin-tan/llm-vul).

## Dataset Description

The original LLM-VUL dataset only includes single-hunk vulnerability fixes transformation. In this folder, we provide:
1. The original single-hunk vulnerability fixes from LLM-VUL
2. Supplementary data that includes multi-hunk vulnerability fixes

## Setup Instructions

To set up the complete dataset:

1. First, download the original LLM-VUL dataset by following the instructions at their [official repository](https://github.com/lin-tan/llm-vul)
2. The supplementary data is already included in this folder

Please refer to the original LLM-VUL repository for detailed information about the dataset format and usage instructions. 

To use the dataset, please cite their paper:

```
@inproceedings{10.1145/3597926.3598135,
author = {Wu, Yi and Jiang, Nan and Pham, Hung Viet and Lutellier, Thibaud and Davis, Jordan and Tan, Lin and Babkin, Petr and Shah, Sameena},
title = {How Effective Are Neural Networks for Fixing Security Vulnerabilities},
year = {2023},
isbn = {9798400702211},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3597926.3598135},
doi = {10.1145/3597926.3598135},
booktitle = {Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis},
pages = {1282–1294},
numpages = {13},
keywords = {Automated Program Repair, Vulnerability, AI and Software Engineering, Language Model},
location = {Seattle, WA, USA},
series = {ISSTA 2023}
}