Software Defect Prediction Using an Intelligent Ensemble-Based Model

Abstract

Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model that combines diverse classifiers. The proposed model employs a two-stage prediction process to detect defective modules. In the first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network. These algorithms are optimized through iterative parameter optimization to achieve the highest accuracy possible. In the second stage, the predictive accuracy of the individual classifiers is integrated into a voting ensemble to make the final predictions. This ensemble approach further improves the accuracy and reliability of the defect predictions. Seven historical defect datasets from the NASA MDP repository, namely CM1, JM1, MC2, MW1, PC1, PC3, and PC4, were utilized to implement and evaluate the proposed defect prediction system. The results demonstrate that each dataset's proposed intelligent system achieved remarkable accuracy, outperforming twenty state-of-the-art defect prediction techniques, including base classifiers and ensemble methods.