Predicting Change Impact in Object-Oriented Applications with Bayesian Networks

This study has to be considered as another step towards the proposal of assessment/predictive models in software quality. We consider in this work, that a probabilistic model using Bayesian nets constitutes an interesting alternative to non-probabilistic models suggested in the literature. Thus, we propose in this paper a probabilistic approach using Bayesian networks to analyze and predict change impact in object-oriented systems. An impact model is built and probabilities are assigned to network nodes. Data obtained from a real system are exploited to empirically study causality hypotheses between some software internal attributes and change impact. Several scenarios are executed on the network, and the obtained results confirm that coupling is a good indicator of change impact.