Heterogeneous Defect Prediction Through Multiple Kernel Learning and Ensemble Learning

Abstract

Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These two data characteristics make it challenging to build an effective HDP model. In this paper, we propose a novel Ensemble Multiple Kernel Correlation Alignment (EMKCA) based approach to HDP, which takes into consideration the two characteristics of the defect prediction data. Specifically, we first map the source and target project data into high dimensional kernel space through multiple kernel leaning, where the defective and non-defective modules can be better separated. Then, we design a kernel correlation alignment method to make the data distribution of the source and target projects similar in the kernel space. Finally, we integrate multiple kernel classifiers with ensemble learning to relieve the influence caused by class imbalance problem, which can improve the accuracy of the defect prediction model. Consequently, EMKCA owns the advantages of both multiple kernel learning and ensemble learning. Extensive experiments on 30 public projects show that EMKCA outperforms the related competing methods.