Defect Prediction Using Stylistic Metrics
This addresses defect prediction for software developers, but it is incremental as it applies existing machine learning methods to a new type of metric.
The paper tackled defect prediction in software by analyzing the impact of programming style metrics, finding that these metrics are a good predictor of defects, with results evaluated using F1, precision, and recall on 14 releases of 5 open-source projects.
Defect prediction is one of the most popular research topics due to its potential to minimize software quality assurance efforts. Existing approaches have examined defect prediction from various perspectives such as complexity and developer metrics. However, none of these consider programming style for defect prediction. This paper aims at analyzing the impact of stylistic metrics on both within-project and crossproject defect prediction. For prediction, 4 widely used machine learning algorithms namely Naive Bayes, Support Vector Machine, Decision Tree and Logistic Regression are used. The experiment is conducted on 14 releases of 5 popular, open source projects. F1, Precision and Recall are inspected to evaluate the results. Results reveal that stylistic metrics are a good predictor of defects.