Defect Prediction Using Stylistic Metrics

TL;DR

This paper investigates how stylistic coding metrics influence defect prediction in software projects, demonstrating that style features can effectively predict defects across different projects using machine learning models.

Contribution

It introduces the use of stylistic metrics for defect prediction and evaluates their effectiveness in within-project and cross-project scenarios.

Findings

Stylistic metrics are effective predictors of software defects.

Support Vector Machine performs well in defect prediction tasks.

Stylistic features improve cross-project defect prediction accuracy.

Abstract

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.