Particle swarm optimization-based ensemble learning for software change prediction

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Abstract Context Various researchers have successfully established the association between Object-Oriented metrics and change prone nature of a class. However, they actively continue to explore effective classifiers for developing efficient change prediction models. Recent developments have ascertained that ensemble methodology can be used to improve the prediction performance of individual classifiers. Objective This study proposes four strategies of ensemble learning to predict change prone classes by combining seven individual Particle Swarm Optimization (PSO) based classifiers as constituents of ensembles and aggregating them using weighted voting. Method The weights allocated to individual classifiers are based on their accuracy and their ability to correctly predict “hard instances” i.e. classes which are frequently misclassified by a majority of classifiers. Each individual PSO based classifier uses a different fitness function. The ensembles are constructed on the premises that change in fitness functions leads to variation in the results of a search-based algorithm such as PSO. Therefore, it is important to combine them to obtain a better classifier with improved accuracy using the ensemble methodology. Results The proposed strategies of ensemble learning were found effective in predicting software change. The statistical analysis of the results indicates improved performance of the proposed ensemble classifiers as compared to individual classifiers. Furthermore, the results of the proposed voting ensemble classifiers were found competent with those of machine-learning ensemble classifiers for determination of change prone classes. Conclusion The accuracy and diversity of the individual classifiers were instrumental in the superior performance of the proposed voting ensemble classifiers.