Defect Prediction Using Stylistic Metrics. (arXiv:2206.10959v2 [cs.SE] UPDATED)

Web: http://arxiv.org/abs/2206.10959

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 …

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