A framework for capturing, statistically modeling and analyzing the evolution of software models

• New framework for capturing, statistically modeling and simulating evolution of models
• Evolution formulated at 2 abstraction levels: low and high level changes between models
• Empirical study of evolution of Java models using 3 kinds of time series models
• Mixed ARMA-GARCH models were superior, but ARMA models performed well in practice
• Statistical models were used to generate more realistic test models for MDE tools

This paper presents a new methodological framework for capturing and statistically modeling the evolution of models in model-driven software development. The framework captures the changes between revisions of models in terms of both low-level (internal) and high-level (developer-visible) edit operations applied between revisions. In our approach, evolution is modeled statistically by using ARMA, GARCH and mixed ARMA-GARCH models. Forecasting and simulation aspects of these time series models are thoroughly assessed. The suitability of the framework is shown by applying it to a large set of design models of real Java systems. Our analysis shows that mixed ARMA-GARCH models are superior to ARMA models.A main motivation for, and application of, the resulting statistical models is to control the generation of realistic model histories which are intended to be used for testing model versioning tools. We present the architecture of the model generator and show how to generate random sequences from the statistical models which control the generation process. Further usages of the statistical models include various forecasting and simulation tasks.