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Software bug count prediction via AdaBoost.R-ET

Finding the bug count in software modules in order to utilized limited testing resources that could help the testers is a challenging task. Mostly the regression methods are used for finding the bug count. AdaBoost is the one of machine learning technique which has been used for both classification and regression problems. However, it cannot perform better if the weak learner cannot accomplish at least 50% accuracy when running on the skewed dataset. Extra Tree regression is computationally effective and delivers excellent prediction performance. In this paper, we present a new approach, AdaBoost.R-ET algorithm to predict the bug count in a software module. We used the extra tree regression as a weak learner of AdaBoost regression to provide a significant improvement over the model. An experimental study is performed on five projects from the PROMISE repository consisting of 15 different versions. To estimate the performance of the AdaBoost.R-ET algorithm, we compare this algorithm with varying models of AdaBoost. The outcomes show that the proposed model is better than the different variants of AdaBoost models.