This is a service built to indicate if some code is defect-prone or not. The service uses an SVM classifier. The model was tested and validaded against 2 different sets, achieving an accuracy of ~98%.
The features present in the dataset, and that have to be provided to the service, are the following:
| Feature | Description |
|---|---|
| loc | numeric % McCabe's line count of code |
| v(g) | numeric % McCabe "cyclomatic complexity" |
| ev(g) | numeric % McCabe "essential complexity" |
| iv(g) | numeric % McCabe "design complexity" |
| n | numeric % Halstead total operators + operands |
| v | numeric % Halstead "volume" |
| l | numeric % Halstead "program length" |
| d | numeric % Halstead "difficulty" |
| i | numeric % Halstead "intelligence" |
| e | numeric % Halstead "effort" |
| b | numeric % Halstead |
| t | numeric % Halstead's time estimator |
| lOCode | numeric % Halstead's line count |
| lOComment | numeric % Halstead's count of lines of comments |
| lOBlank | numeric % Halstead's count of blank lines |
| lOCodeAndComment | numeric |
| uniq_Op | numeric % unique operators |
| uniq_Opnd | numeric % unique operands |
| total_Op | numeric % total operators |
| total_Opnd | numeric % total operands |
| branchCount | numeric % of the flow graph |
The service endpoint is https://code-defect-ml.herokuapp.com/predict
Method: POST
Sample JSON payloads:
[129,30,18,25,402,2583.36,0.02,46.42,55.65,119927.45,0.86,6662.64,111,1,14,1,34,52,260,142,58]
[20,2,1,2,47,212.61,0.22,4.59,46.28,976.67,0.07,54.26,11,1,4,1,7,16,26,21,3]
Sample service response:
{
"prediction": [
1
]
}
Obs: A prediction 1 indicates true for defect-prone code. A prediction 0 indicates false for defect-prone code.