Referencias:
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:code_x_glue_cc_defect_detection')
- Descripción :
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.
- Licencia : Ninguna licencia conocida
- Versión : 0.0.0
- Divisiones :
| Dividir | Ejemplos |
|---|---|
'test' | 2732 |
'train' | 21854 |
'validation' | 2732 |
- Características :
{
"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"
}
}