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README.md

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+# 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>