semeru/code-code-DefectDetection
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… See the full description on the dataset page: https://huggingface.co/datasets/semeru/code-code-DefectDetection.
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1---2license: mit3Programminglanguage: "C"4version: "N/A"5Date: "Devign(Jun 2019 - paper release date)"6Contaminated: "Very Likely"7Size: "Standard Tokenizer"8---9 10### Dataset is imported from CodeXGLUE and pre-processed using their script.11 12# Where to find in Semeru:13The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru14 15 16 17# CodeXGLUE -- Defect Detection18 19## Task Definition20 21Given 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.22 23### Dataset24 25The 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.26 27 28### Data Format29 30Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present31 32For each file, each line in the uncompressed file represents one function. One row is illustrated below.33 34 - **func:** the source code35 - **target:** 0 or 1 (vulnerability or not)36 - **idx:** the index of example37 38### Data Statistics39 40Data statistics of the dataset are shown in the below table:41 42| | #Examples |43| ----- | :-------: |44| Train | 21,854 |45| Dev | 2,732 |46| Test | 2,732 |47 48 49## Reference50<pre><code>@inproceedings{zhou2019devign,51 title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},52 author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},53 booktitle={Advances in Neural Information Processing Systems},54 pages={10197--10207},55 year={2019}56}</code></pre>57