Investigation of Ensemble Learners for Change Prediction in Android Operating System
As a software system evolves, the changes are introduced at every stage of its development. The timely identification of the classes which are prone to changes is very important to reduce the costs associated with the maintenance phase. Thus, the author has developed various models which can be used in the design phase (one of the early phases) to identify the parts of software which are more change prone than others. Software metrics along with the change data can be used for developing the models. In this study, the author aims to investigate the performance of various ensemble learners
and a statistical model for identifying the classes which are change prone. The use of ensemble learners give the researchers an opportunity to analyze and investigate them in the area of change prediction. The empirical validation is carried on 5 official releases of Android operating system. The overall results of the study indicate that the ensemble learners are capable of effective prediction.
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