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