|
--- |
|
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](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). 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 |
|
<pre><code>@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} |
|
}</code></pre> |
|
|
