  Cross-project defect prediction (CPDP) aims to predict defects of projects
lacking training data by using prediction models trained on historical defect
data from other projects. However, since the distribution differences between
datasets from different projects, it is still a challenge to build high-quality
CPDP models. Unfortunately, class imbalanced nature of software defect datasets
further increases the difficulty. In this paper, we propose a transferlearning
oriented minority over-sampling technique (TOMO) based feature weighting
transfer naive Bayes (FWTNB) approach (TOMOFWTNB) for CPDP by considering both
classimbalance and feature importance problems. Differing from traditional
over-sampling techniques, TOMO not only can balance the data but reduce the
distribution difference. And then FWTNB is used to further increase the
similarity of two distributions. Experiments are performed on 11 public defect
datasets. The experimental results show that (1) TOMO improves the average
G-Measure by 23.7\%$\sim$41.8\%, and the average MCC by 54.2\%$\sim$77.8\%. (2)
feature weighting (FW) strategy improves the average G-Measure by 11\%, and the
average MCC by 29.2\%. (3) TOMOFWTNB improves the average G-Measure value by at
least 27.8\%, and the average MCC value by at least 71.5\%, compared with
existing state-of-theart CPDP approaches. It can be concluded that (1) TOMO is
very effective for addressing class-imbalance problem in CPDP scenario; (2) our
FW strategy is helpful for CPDP; (3) TOMOFWTNB outperforms previous
state-of-the-art CPDP approaches.
