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Cross-project defect prediction (CPDP) has been deemed as an emergingtechnology of software quality assurance, especially in new or inactiveprojects, and a few improved methods have been proposed to support betterdefect prediction. However, the regular CPDP always assumes that the featuresof training and test data are all identical. Hence, very little is known aboutwhether the method for CPDP with imbalanced feature sets (CPDP-IFS) works well.Considering the diversity of defect data sets available on the Internet as wellas the high cost of labeling data, to address the issue, in this paper weproposed a simple approach according to a distribution characteristic-basedinstance (object class) mapping, and demonstrated the validity of our methodbased on three public defect data sets (i.e., PROMISE, ReLink and AEEEM).Besides, the empirical results indicate that the hybrid model composed of CPDPand CPDP-IFS does improve the prediction performance of the regular CPDP tosome extent.

Towards Cross-Project Defect Prediction with Imbalanced Feature Sets