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