Aligned metric representation based balanced multiset ensemble learning for heterogeneous defect prediction

Context: Heterogeneous defect prediction (HDP) refers to the defect prediction across projects with different metrics. Most existing HDP methods map source and target data into a common metric space where each dimension has no actual meaning, which weakens their interpretability. Besides, HDP always suffers from the class imbalance problem. Objective: For deficiencies of current HDP methods, we intend to propose a novel HDP approach that can reduce the heterogeneity of source and target data and deal with imbalanced data while retaining the actual meaning for each dimension of constructed common metric space. Method: We propose an Aligned Metric Representation based Balanced Multiset Ensemble learning (BMEL+ AMR) approach for HDP. AMR consists of shared, source-specific, and target-specific metrics. It is built by learning the translation from shared to specific metrics and reducing the distribution difference. To deal with imbalanced data, we design BMEL that constructs multiple balanced subsets for source data and produces an aggregated classifier for predicting labels of target data. Result: Experimental results on 22 public projects indicate that (1) among all competing methods, BMEL+AMR achieves the best performance on all indicators except Popt, followed by AMR; (2) compared with AMR, the introduction of BMEL improves the performance on non-effort-aware indicators statistically significantly except F1-score; compared with BMEL, the introduction of AMR improves the performance throughout all indicators statistically significantly. Conclusion: BMEL+AMR can effectively improve HDP performance by eliminating heterogeneity and dealing with imbalanced data, and AMR is helpful to explain the prediction model.