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

arXiv: https://arxiv.org/abs/2306.15369 · HTML: https://mlsanity.com/paper/2306.15369/

## Key facts

- Cited by 1 papers 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 (most cited first, 3 of 3)

- [An Empirical Study on Software Defect Prediction with a Simplified Metric Set](https://mlsanity.com/paper/1402.3873/) (2014)
- [Benchmarking Machine Learning Technologies for Software Defect Detection](https://mlsanity.com/paper/1506.07563/) (2015)
- [The Integrity of Machine Learning Algorithms against Software Defect Prediction](https://mlsanity.com/paper/2009.02571/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Survey on SAR ship classification using Deep Learning](https://mlsanity.com/paper/2503.11906/) (2025)

## 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.