Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification

As the typical application of computational intelligence in software engineering, cross-project defect prediction (CPDP) uses labeled data from other projects (source projects) for building models to predict the defects in the current projects (target projects), helping testers quickly locate the defective modules. But class imbalance and different data distribution among projects make CPDP a challenging topic. To address the above two problems, we propose a two-phase feature importance amplification (TFIA) CPDP model in this paper which can solve these two problems from domain adaptation phase and classification phase. In the domain adaptation phase, the differences in data distribution among projects are reduced by filtering both source and target projects, and the correlation-based feature selection with greedy best-first search amplifies the importance of features with strong feature-class correlation. In the classification phase, Random Forest works as the classifier to further amplify the importance of highly correlated features and establish a model which is sensitive to highly correlated features. We conducted both ablation experiments and comparison experiments on the widely used AEEEM database. Experimental results show that TFIA can yield significant improvement on CPDP. And the performance of TFIA CPDP model in all experiments is stable and efficient, which lays a solid foundation for its further application in practical engineering.