Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques
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Software today has become an inseparable part of our life. In order to achieve the ever demanding needs ofcustomers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study therelationship of object oriented metrics with change proneness attribute of a class. Prediction models based onthis study can help us in identifying change prone classes of a software. We can then focus our efforts on thesechange prone classes during testing to yield a better quality software. Previously, researchers have used statisticalmethods for predicting change prone classes. But machine learning methods are rarely used for identificationof change prone classes. In our study, we evaluate and compare the performances of ten machine learningmethods with the statistical method. This evaluation is based on two open source software systems developedin Java language. We also validated the developed prediction models using other software data set in thesame domain (3D modelling). The performance of the predicted models was evaluated using receiver operatingcharacteristic analysis. The results indicate that the machine learning methods are at par with the statisticalmethod for prediction of change prone classes. Another analysis showed that the models constructed for asoftware can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will alsohelp the developers to design an effective model that results in less change prone classes, hence better maintenance.
Abstract
Software today has become an inseparable part of our life. In order to achieve the ever demanding needs ofcustomers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study therelationship of object oriented metrics with change proneness attribute of a class. Prediction models based onthis study can help us in identifying change prone classes of a software. We can then focus our efforts on thesechange prone classes during testing to yield a better quality software. Previously, researchers have used statisticalmethods for predicting change prone classes. But machine learning methods are rarely used for identificationof change prone classes. In our study, we evaluate and compare the performances of ten machine learningmethods with the statistical method. This evaluation is based on two open source software systems developedin Java language. We also validated the developed prediction models using other software data set in thesame domain (3D modelling). The performance of the predicted models was evaluated using receiver operatingcharacteristic analysis. The results indicate that the machine learning methods are at par with the statisticalmethod for prediction of change prone classes. Another analysis showed that the models constructed for asoftware can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will alsohelp the developers to design an effective model that results in less change prone classes, hence better maintenance.
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