Given that our system had never been used for defect prediction until now, we conclude that {\W} can be very successful in certain cases and not very successful in others. We believe these results point to a deeper issue, namely that successful defect prediction seems somewhat dataset dependent. What we mean by this is that in order to optimally predict for fault-proneness, a learner needs to be customized for the type of data in question.

As far as which metrics we found to be relevant in predicting for defects, we can only conclude that some of the metrics usually associated with defect prediction are indeed relevant in the context of our datasets while others are not. We have reasons to believe there might still be issues to be worked out with the {\W} system, particularly in the way rules are scored, however the consistency of some of our results, in particular the prop and xerxes datasets, points to {\W}'s potential in defect prediction.
