Behavioural Model Fusion

Behavioural Model Fusion Shiva Nejati Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 In large-scale model-based development, developers periodically need to combine collections of interrelated models. These models may capture different features of a system, describe alternative perspectives on a single feature, or express ways in which different features alter one another’s structure or behaviour. We refer to the process of combining a set of interrelated models as model fusion. A number of factors make model fusion complicated. Models may overlap, in that they refer to the same concepts, but these concepts may be presented differently in each model, and the models may contradict one another. Models may describe independent system components, but the components may interact, potentially causing undesirable side effects. Finally, models may cross-cut, modifying one another in ways that violate their syntactic or semantic properties. In this thesis, we study three instances of the fusion problem for behavioural models, motivated by real-world applications. The first problem is combining partial models of a single feature with the goal of creating a more complete description of that feature. The second problem is maintenance of variant specifications of individual features. The goal here is to combine the variants while preserving their points of difference (i.e., variabilities). The third problem is analysis of interactions between models describing different features. Specifically, given a set of features, the goal is to construct a composition such that undesirable interactions are absent. We provide an automated tool-supported