A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion

This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques.