Supplementing Object-Oriented software change impact analysis with fault-proneness prediction

Software changes are inevitable during maintenance, Object-oriented software (OOS) in particular. For change not to be performed in the “dark”, software change impact analysis (SCIA) is used. However, due to the exponential growth in the size and complexity of OOS, classes are not without faults and the existing SCIA techniques only predict change impact set. This means that a change implemented on a faulty class could increase the likelihood for software failure. To avoid this issue, maintenance has to incorporate both change impact and fault-proneness (FP) prediction. Therefore, this paper proposes an extended approach for SCIA that integrates both activities. The goal is to assist software engineers with the necessary information of focusing verification and validation activities on the high risk components that would probably cause severe failures which in turn can boost maintenance and testing efficiency. This study built a model for predicting FP using software metrics and faults data from NASA data set in the public domain. The results obtained were analyzed and presented. Additionally, a class change recommender (CCRecommender) tool was developed to assist in computing the risks associated with making change to any component in the impact set.