MVSE: Effort-Aware Heterogeneous Defect Prediction via Multiple-View Spectral Embedding

Cross-Project Defect Prediction (CPDP) predicts defects in a target project using the defect information of the external project. Existing CPDP methods assume that the data of two projects share identical features. When cross-project data contain heterogeneous features, traditional CPDP methods become ineffective. In this paper, we propose a novel approach called Multiple-View Spectral Embedding (MVSE) to address the heterogeneous CPDP issue. MVSE treats the cross-project data as two different views and exploits the spectral embedding method to map the heterogeneous feature sets into a consistent space where the two mapped feature sets have maximal similarity. To evaluate MVSE in the realistic setting, we employ an effort-aware performance indicator that considers the cost of inspection in the context of heterogeneous CPDP scenario. We have conducted extensive experiments to compare MVSE with two state-of-the-art heterogeneous CPDP methods and within-project setting. The experiments on 94 cross project pairs show that MVSE achieves promising results.