license: mit
Programminglanguage: C
version: N/A
Date: Devign(Jun 2019 - paper release date)
Contaminated: Very Likely
Size: Standard Tokenizer
Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru
CodeXGLUE -- Defect Detection
Task Definition
Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
Dataset
The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.
Data Format
Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- func: the source code
- target: 0 or 1 (vulnerability or not)
- idx: the index of example
Data Statistics
Data statistics of the dataset are shown in the below table:
| #Examples | |
|---|---|
| Train | 21,854 |
| Dev | 2,732 |
| Test | 2,732 |
Reference
@inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={10197--10207},
year={2019}
}