Heterogeneous Defect Prediction through Correlation-Based Selection of Multiple Source Projects and Ensemble Learning

Heterogeneous defect prediction (HDP) predicts defect-prone modules when the source and target data have heterogeneous metric sets. Although several researchers have tried to improve the performance of HDP, many of them did not suggest selection guidelines of source projects nor handle the class imbalance problem. In this paper, we propose a novel approach to improve the performance further by selecting proper source projects for the given target project and considering imbalanced data, called CorrelAtion-based selection of Multiple source projects and Ensemble Learning (CAMEL) for HDP. Specifically, CAMEL first matches metrics through the Kolmogorov-Smirnov test. Second, it calculates fitness scores based on correlation analysis and selects multiple projects. Third, it predicts target labels using each selected source project and integrates the results with ensemble learning. The experiments show that CAMEL produces better results against existing methods. Consequently, CAMEL enhances reliability in the early development phase by providing proper source selection guidelines.