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  license: mit
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  license: mit
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+ # CodeXGLUE -- Defect Detection
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+ ## Task Definition
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+ 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.
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+ ### Dataset
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+ 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.
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+ ### Data Format
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+ Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present
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+ For each file, each line in the uncompressed file represents one function. One row is illustrated below.
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+ - **func:** the source code
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+ - **target:** 0 or 1 (vulnerability or not)
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+ - **idx:** the index of example
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+ ### Data Statistics
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+ Data statistics of the dataset are shown in the below table:
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+ | | #Examples |
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+ | ----- | :-------: |
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+ | Train | 21,854 |
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+ | Dev | 2,732 |
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+ | Test | 2,732 |
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+ ## Reference
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+ <pre><code>@inproceedings{zhou2019devign,
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+ title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
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+ author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
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+ booktitle={Advances in Neural Information Processing Systems},
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+ pages={10197--10207},
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+ year={2019}
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+ }</code></pre>