mlsanityData to 14 Oct 2026

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

arXiv 2306.15369 ↗First posted 27 Jun 2023cs.SE cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 29 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

3 of 29 references.

References · 3

  1. 2014An Empirical Study on Software Defect Prediction with a Simplified Metric Setcited by 5
  2. 2015Benchmarking Machine Learning Technologies for Software Defect Detectioncited by 4
  3. 2020The Integrity of Machine Learning Algorithms against Software Defect Predictioncited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.