Effective multi-objective naïve Bayes learning for cross-project defect prediction

Software defect prediction predicts fault-prone modules which will be tested thoroughly. Thereby, limited quality control resources can be allocated effectively on them. Without sufficient local data, defects can be predicted via cross-project defect prediction (CPDP) utilizing data from other projects to build a classifier. Software defect datasets have the class imbalance problem, indicating the defect class has much fewer instances than the non-defect class does. Unless defect instances are predicted correctly, software quality could be degraded. In this context, a classifier requires to provide high accuracy of the defect class without severely worsening the accuracy of the non-defect class. This class imbalance principle seamlessly connects to the purpose of the multi-objective (MO) optimization in that MO predictive models aim at balancing many of the competing objectives. In this paper, we target to identify effective multi-objective learning techniques under cross-project (CP) environments. Three objectives are devised considering the class imbalance context. The first objective is to maximize the probability of detection (PD). The second objective is to minimize the probability of false alarm (PF). The third objective is to maximize the overall performance (e.g., Balance). We propose novel MO naive Bayes learning techniques modeled by a Harmony Search meta-heuristic algorithm. Our approaches are compared with single-objective models, other existing MO models and within-project defect prediction models. The experimental results show that the proposed approaches are promising. As a result, they can be effectively applied to satisfy various prediction needs under CP settings.