Identification and analysis of change ripples in object-oriented software applications

Software development and maintenance accompany several challenges related to change management. Identifying dependencies of change-prone classes helps to manage the after-effects of changes smoothly. This paper aims to study the ripple effect identification in object-oriented software applications using software metrics and change history. The changeability pattern is generated and compared with actual changes to validate the effectiveness of the proposed approach for ripple effect identification. The impact set of existing classes is derived using the change history with a commit weight-based approach. Two coupling measures, Likelihood of Change (LiCh) and Co-change Probability (CChPr), are derived to analyse the change impact set of existing classes. The change impact of new classes is derived using a Bagging classification technique. The source code metrics are independent variables and co-change derived from change history is the dependent variable for the prediction model. The results indicate that most dependent classes are identified using the proposed technique and advocate using software metrics and change history for ripple effect identification. It can be beneficial for software practitioners to understand the impact of change and identify dependencies of an explicit class.