Analysis and Prediction of Design Model Evolution Using Time Series

Tools which support Model-Driven Engineering have to be evaluated and tested. In the domain of model differencing and model versioning, sequences of software models (model histories), in which a model is obtained from its immediate predecessor by some modification, are of special interest. Unfortunately, in this application domain adequate real test models are scarcely available and must be artificially created. To this end, model generators were proposed in recent years. Generally, such model generators should be configured in a way that the generated sequences of models are as realistic as possible, i.e. they should mimic the changes that happen in real software models. Hence, it is a necessary prerequisite to analyze and to stochastically model the evolution (changes) of real software systems at the abstraction level of models. In this paper, we present a new approach to statistically analyze the evolution of models. Our approach uses time series as a statistical method to capture the dynamics of the evolution. We applied this approach to several typical projects and we successfully modeled their evolutions. The time series models could predict the future changes of the next revisions of the systems with good accuracies. The obtained time series models are used to create more realistic model histories for model versioning and model differencing tools.