  Until now, researchers have proposed several novel heterogeneous defect
prediction HDP methods with promising performance. To the best of our
knowledge, whether HDP methods can perform significantly better than
unsupervised methods has not yet been thoroughly investigated. In this article,
we perform a replication study to have a holistic look in this issue. In
particular, we compare state-of-the-art five HDP methods with five unsupervised
methods. Final results surprisingly show that these HDP methods do not perform
significantly better than some of unsupervised methods (especially the simple
unsupervised methods proposed by Zhou et al.) in terms of two non-effort-aware
performance measures and four effort-aware performance measures. Then, we
perform diversity analysis on defective modules via McNemar's test and find the
prediction diversity is more obvious when the comparison is performed between
the HDP methods and the unsupervised methods than the comparisons only between
the HDP methods or between the unsupervised methods. This shows the HDP methods
and the unsupervised methods are complementary to each other in identifying
defective models to some extent. Finally, we investigate the feasibility of
five HDP methods by considering two satisfactory criteria recommended by
previous CPDP studies and find the satisfactory ratio of these HDP methods is
still pessimistic. The above empirical results implicate there is still a long
way for heterogeneous defect prediction to go. More effective HDP methods need
to be designed and the unsupervised methods should be considered as baselines.
