References:
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:code_x_glue_cc_defect_detection')
- Description:
CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection
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.
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.
- License: No known license
- Version: 0.0.0
- Splits:
Split | Examples |
---|---|
'test' |
2732 |
'train' |
21854 |
'validation' |
2732 |
- Features:
{
"id": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"func": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"target": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"project": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"commit_id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}