metadata
dataset_info:
features:
- name: file_name
dtype: int64
- name: vulnerable_line_numbers
dtype: string
- name: dataset_type
dtype: string
- name: commit_hash
dtype: string
- name: unique_id
dtype: int64
- name: project
dtype: string
- name: target
dtype: int64
- name: repo_url
dtype: string
- name: date
dtype: string
- name: code
dtype: string
- name: CVE
dtype: string
- name: CWE
dtype: string
- name: commit_link
dtype: string
- name: severity
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1440079604
num_examples: 128705
- name: test
num_bytes: 1668696730
num_examples: 142214
download_size: 1076557341
dataset_size: 3108776334
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
🔎 Details
This is a C++ vulnerability detection dataset following realistic settings. For details, please check our study Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets (Partha et al., 2024)
The column names are self-describing. The most important two columns are,
target: int: vulnerable to not.code: str: the code segment.
📄 Citation Information
@article{Chakraborty2024,
title = {Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets},
ISSN = {2326-3881},
url = {http://dx.doi.org/10.1109/TSE.2024.3423712},
DOI = {10.1109/tse.2024.3423712},
journal = {IEEE Transactions on Software Engineering},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Chakraborty, Partha and Arumugam, Krishna Kanth and Alfadel, Mahmoud and Nagappan, Meiyappan and McIntosh, Shane},
year = {2024},
pages = {1–15}
}