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

Autor:	Awais, Ch Muhammad, Gu, Wei, Dlamini, Gcinizwe, Kholmatova, Zamira, Succi, Giancarlo
Rok vydání:	2023
Předmět:	Computer Science - Software Engineering Computer Science - Machine Learning
Zdroj:	Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716
Druh dokumentu:	Working Paper
Popis:	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. Comment: 11 pages, 8 figures, Conference Paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.15369 Zobrazit plný text záznamu View this record from Arxiv