---
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)](https://arxiv.org/abs/2407.03093) 

The column names are self-describing. The most important two columns are,

1. `target: int`: vulnerable to not.
2. `code: str`: the code segment.



## 📄 Citation Information

```bibtex
@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}
}
```