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

Abstract

In this work, we report about a recently developed framework for capturing, statistically modeling and analyzing the evolution of software models, published in the Journal of Systems and Software, Vol-118, Aug-2016. State-of-the-art approaches to understand the evolution of models of software systems are based on software metrics and similar static properties; the extent of the changes between revisions of a software system is expressed as differences of metrics values, and statistical analyses are based on these differences. Unfortunately, such approaches do not properly reflect the dynamic nature of changes. In contrast to this, our 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. Evolution is modeled statistically by using ARMA, GARCH and mixed ARMA-GARCH time series models. Forecasting and simulation aspects of these time series models are thoroughly assessed, and the suitability of the framework is shown by applying it to a large set of design models of real Java systems. 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. Further usages of the statistical models include various forecasting and simulation tasks.