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download_size: 33528231
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dataset_size: 156327244
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Dataset is imported from CodeXGLUE and pre-processed using their script.
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Where to find in Semeru:
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The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru
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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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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
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target
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idx
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Data statistics of the dataset are shown in the below table:
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download_size: 33528231
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dataset_size: 156327244
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### Dataset is imported from CodeXGLUE and pre-processed using their script.
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# Where to find in Semeru:
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The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru
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# CodeXGLUE -- Defect Detection
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## [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#task-definition)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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### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#dataset)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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### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#data-format)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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### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#data-statistics)Data Statistics
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Data statistics of the dataset are shown in the below table:
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| | Description |
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| ----------- | ----------- |
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| Train| 126,477|
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| Dev| 15,809|
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|Test|15,810
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