Dynamic Selection an Classifier for Software Fault Prediction

In the last decades, the research community has devoted a lot of effort in the definition of approaches able to predict the defect proneness of source code files. Such approaches exploit several predictors (e.g., product or process metrics) and use machine learning classifiers to predict classes into buggy or not buggy, or provide the likelihood that a class will exhibit a fault in the near future. The empirical evaluation of all these approaches indicated that there is no machine learning classifier providing the best accuracy in any context, highlighting interesting complementarity among them. For these reasons ensemble methods have been proposed to estimate the bug-proneness of a class by combining the predictions of different classifiers. Following this line of research, in this paper we propose an adaptive method, named ASCI (Adaptive Selection of Classifiers in bug prediction), able to dynamically select among a set of machine learning classifiers the one which better predicts the bug-proneness of a class based on its characteristics. An empirical study conducted on 30 software systems indicates that ASCI exhibits higher performances than five different classifiers used independently and combined with the majority voting ensemble method.