A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction

TL;DR

This study systematically compares Naive Bayes and Random Forest models for software defect prediction, finding no significant performance difference in key metrics across analyzed studies.

Contribution

It provides a meta-analytical comparison of the two models, clarifying their relative effectiveness in defect prediction tasks.

Findings

No significant difference in recall, f-measure, and precision between models.

Meta-analysis based on five studies.

Systematic literature review methodology used.

Abstract

Is there a statistical difference between Naive Bayes and Random Forest in terms of recall, f-measure, and precision for predicting software defects? By utilizing systematic literature review and meta-analysis, we are answering this question. We conducted a systematic literature review by establishing criteria to search and choose papers, resulting in five studies. After that, using the meta-data and forest-plots of five chosen papers, we conducted a meta-analysis to compare the two models. The results have shown that there is no significant statistical evidence that Naive Bayes perform differently from Random Forest in terms of recall, f-measure, and precision.