Software defect prediction using augmented bayesian networks

Abstract

Prediction models are built with various machine learning algorithms to identify defects prior to release to facilitate software testing, and save testing costs. Naïve Bayes classifier is one of the best performing classification techniques in defect prediction. It assumes conditional independence of features and for defect prediction problem some of the features are not actually conditionally independent. The interesting problem is to relax these conditional independence assumptions and to check whether there is any improvement in performance of classifiers. We have built Bayesian Network structures using different classes of algorithms namely score-based, constraint-based and hybrid algorithms. We propose an approach to augment these Bayesian Network structures with class node. Bayesian Network classifiers along with Random Forests, Logistic Regression and Naïve Bayes classifiers are then evaluated using measures like AUC and H-measure. We observe that RSMAX2 and Grow-Shrink classifiers (after augmentation) perform consistently better in defect prediction.