Union Models: Support for Efficient Reasoning About Model Families Over Space and Time

Abstract

For a given modeling language, a model family is a set of related models, with commonalities and variabilities among family members, that results from the variation/evolution of models over the space and time dimensions. With large model families, the analysis of individual models becomes cumbersome and inefficient. This paper proposes union models as a paradigm supporting the representation of model families (for time and space dimensions) using one generic model. Elements of a union model are annotated with information about time and space using a new spatio-temporal annotation language (STAL) in order to distinguish which element belongs to which model. We demonstrate empirically the usefulness of union models for analyzing a family of models, all at once, compared to individual models, one model at a time. Our experiments suggest that the use of union models facilitate efficient analysis in several contexts.