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--- |
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dataset_info: |
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features: |
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- name: input |
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dtype: string |
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- name: output |
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dtype: string |
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splits: |
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- name: validation |
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num_bytes: 15586336 |
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num_examples: 15809 |
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- name: train |
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num_bytes: 125099945 |
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num_examples: 126477 |
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- name: test |
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num_bytes: 15640963 |
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num_examples: 15810 |
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download_size: 33528231 |
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dataset_size: 156327244 |
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--- |
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# Dataset Card for "AGabs_finetuning" |
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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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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. 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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Train 126,477 |
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Dev 15,809 |
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Test 15,810 |
