A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction
This provides clarity for software engineering researchers and practitioners on model selection, though it is incremental as it synthesizes existing evidence.
The study tackled the problem of comparing Naive Bayes and Random Forest for software defect prediction using meta-analysis, finding no significant statistical difference in recall, f-measure, and precision between the two models.
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.