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Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques

Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques

LinRuchika Malhotra, Ankita Jain Bansal
Copyright: © 2014 |Pages: 10
ISBN13: 9781466644908|ISBN10: 1466644907|EISBN13: 9781466644915
DOI: 10.4018/978-1-4666-4490-8.ch019
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MLA

Malhotra, LinRuchika, and Ankita Jain Bansal. "Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques." Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability, edited by Francisco Vicente Cipolla Ficarra, IGI Global, 2014, pp. 193-202. https://doi.org/10.4018/978-1-4666-4490-8.ch019

APA

Malhotra, L. & Bansal, A. J. (2014). Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques. In F. Cipolla Ficarra (Ed.), Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability (pp. 193-202). IGI Global. https://doi.org/10.4018/978-1-4666-4490-8.ch019

Chicago

Malhotra, LinRuchika, and Ankita Jain Bansal. "Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques." In Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability, edited by Francisco Vicente Cipolla Ficarra, 193-202. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4490-8.ch019

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Abstract

For software development, availability of resources is limited, thereby necessitating efficient and effective utilization of resources. This can be achieved through prediction of key attributes, which affect software quality such as fault proneness, change proneness, effort, maintainability, etc. The primary aim of this chapter is to investigate the relationship between object-oriented metrics and change proneness. Predicting the classes that are prone to changes can help in maintenance and testing. Developers can focus on the classes that are more change prone by appropriately allocating resources. This will help in reducing costs associated with software maintenance activities. The authors have constructed models to predict change proneness using various machine-learning methods and one statistical method. They have evaluated and compared the performance of these methods. The proposed models are validated using open source software, Frinika, and the results are evaluated using Receiver Operating Characteristic (ROC) analysis. The study shows that machine-learning methods are more efficient than regression techniques. Among the machine-learning methods, boosting technique (i.e. Logitboost) outperformed all the other models. Thus, the authors conclude that the developed models can be used to predict the change proneness of classes, leading to improved software quality.

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