Prediction of change prone classes using evolution-based and object-oriented metrics

• Published in: Journal of intelligent & fuzzy systems 2018-01, Vol.34 (3), p.1755-1766
• Main Authors: Malhotra, Ruchika, Khanna, Megha
• Format: Article
• Language: English
• Subjects: Evolution; Object oriented programming; Software
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-169468

Determination of change prone classes is crucial in providing guidance to software practitioners for efficient allocation of limited resources and to develop favorable quality software products with optimum costs. Previous literature studies have proposed successful use of design metrics to predict classes which are more prone to change in an Object-Oriented (OO) software. However, the use of evolution-based metrics suite, which quantifies history of changes in a software, release by release should also be evaluated for effective prediction of change prone classes. Evolution-based metrics are representative of evolution characteristics of a class over all its previous releases and are important in order to understand progression and change-prone nature of a class. This study evaluates the use of evolution-based metrics when used in conjunction with OO metrics for prediction of classes which are change prone in nature. In order to empirically validate the results, the study uses two application packages of the Android software namely Contacts and Gallery2. The results indicate that evolution based metrics when used in conjunction with OO metrics are the best predictors of change prone classes. Furthermore, the study statistically evaluates the superiority of this combined metric suite for change proneness prediction.