Identifying and Visualising Commonality and Variability in Model Variants

Abstract

Models, as any other software artifact, evolve over time during the development life-cycle. Different versions of the same model are thus existing at different times. Model comparison of different versions has received a lot of attention in recent years. However, existing techniques focus on comparing only two model versions at the same time to identify model differences. Independently of model versioning context, another dimension of variation, called variation in space, appears in models. Contrary to variation in time, variation in space means that a set of model variants exists and should be maintained. Comparing all these model variants to identify common and variable elements becomes thus a major challenge. Current approaches for model variants comparison lack of flexibility and appropriate visualisation paradigm. The contribution of this paper is the Model Variants Comparison approach (MoVaC). This approach compares a set of model variants and identifies both commonality and variability in the form of what is referred to as features. Each feature consists in a set of atomic model-elements. MoVaC also visualizes the identified features using a graphical representation where common and variable features are explicitly presented to users. We validate the approach on two use cases demonstrating the flexibility of MoVaC to be applied to any kind of EMF-based model variants.