A Replication Study: Just-in-Time Defect Prediction with Ensemble Learning

Just-in-time defect prediction, which is also known as change-level defect prediction, can be used to efficiently allocate resources and manage project schedules in the software testing and debugging process. Just-in-time defect prediction can reduce the amount of code to review and simplify the assignment of developers to bug fixes. This paper reports a replicated experiment and an extension comparing the prediction of defect-prone changes using traditional machine learning techniques and ensemble learning. Using datasets from six open source projects, namely Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL we replicate the original approach to verify the results of the original experiment and use them as a basis for comparison for alternatives in the approach. Our results from the replicated experiment are consistent with the original. The original approach uses a combination of data preprocessing and a two-layer ensemble of decision trees. The first layer uses bagging to form multiple random forests. The second layer stacks the forests together with equal weights. Generalizing the approach to allow the use of any arbitrary set of classifiers in the ensemble, optimizing the weights of the classifiers, and allowing additional layers, we apply a new deep ensemble approach, called deep super learner, to test the depth of the original study. The deep super learner achieves statistically significantly better results than the original approach on five of the six projects in predicting defects as measured by F1 score.