| | --- |
| | 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 |
