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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Software defect prediction is an important area in software engineering because it helps developers identify and fix problems before they become costly and hard-to-fix bugs. Early detection of software defects helps save time and money in the software development process and ensures the quality of the final product. This research aims to evaluate three algorithms to build Bayesian Networks to classify whether a project is prone to defects. The choice is based on the fact that the most used approach in the literature is Naive Bayes, but no works use Bayesian Networks. Thus, K2, Hill Climbing, and TAN are used to construct Bayesian Networks. On the other hand, three public PROMISE data sets are used based on McCabe and Halstead complexity metrics. The results are compared with the most used approaches in the literature, such as Decision Tree and Random Forest. The results from different performance metrics applied to a cross-validation process show that the classification results are comparable to Decision Tree and Random Forest, with the advantage that Bayesian algorithms show less variability, which helps engineering software to have greater robustness in their predictions since the selection of training and test data do not give variable results, unlike Decision Tree and Random Forest.

Details

Title
Software Defect Prediction with Bayesian Approaches
Author
Hernández-Molinos, María José 1 ; Sánchez-García, Angel J 1   VIAFID ORCID Logo  ; Barrientos-Martínez, Rocío Erandi 2 ; Pérez-Arriaga, Juan Carlos 1   VIAFID ORCID Logo  ; Jorge Octavio Ocharán-Hernández 1   VIAFID ORCID Logo 

 Facultad de Estadística e Informática, Universidad Veracruzana, Xalapa 91020, Veracruz, Mexico; [email protected] (M.J.H.-M.); [email protected] (J.C.P.-A.); [email protected] (J.O.O.-H.) 
 Instituto de Investigaciones en Inteligencia Artificial, Universidad Veraruzana, Xalapa 91097, Veracruz, Mexico; [email protected] 
First page
2524
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
22277390
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2824015631
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.