| | --- |
| | 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> |
| |
|
