In general, one of the most difficult aspects of ML is collecting data for learning. This is because the more learn-ing data, the higher the prediction accuracy of ML. In this regard, we expected that the combination of PSS, which can easily generate numerous tests, and ML, which finds regularity based on collected data, could exert fantastic synergy. And we were able to dramatically increase the verification coverage by being able to freely create concur-rent function events that were previously considered impossible at the simulation level through PSS and ML