Authors: Ruchika Malhotra; Ankita Jain Bansal
Addresses: Department of Software Engineering, Delhi Technological University, Delhi, India ' Department of Software Engineering, Delhi Technological University, Delhi, India
Abstract: In industrial organisations, software products are quite large and complex, consisting of a number of classes. Thus, it is not possible to test all the products with a finite number of resources. Hence, it would be beneficial if we could predict in advance some of the attributes associated with the classes such as change proneness, defect proneness, maintenance effort, etc. In this paper, we have dealt with one of the quality attributes, i.e., change proneness. Changes in the software are unavoidable and thus, early prediction of change proneness will help the developers to focus the limited resources on the classes which are predicted to be change-prone. We have conducted a systematic review which evaluates all the available important studies relevant to the area of change proneness. This will help us to identify gaps in the current technology and discuss possible new directions of research in the areas related to change proneness.
Keywords: empirical validation; change prediction; machine learning; object-oriented metrics; software quality; software changes; literature review; software development.
International Journal of Computer Applications in Technology, 2016 Vol.54 No.4, pp.240 - 256
Received: 21 Mar 2015
Accepted: 28 Mar 2015
Published online: 17 Nov 2016 *