Scope in model transformations

A notion of hierarchical scope is commonplace in many programmatic systems. In the context of model, and in particular graph transformation, the use of scope can present two advantages: first, more natural expression of transformation application locality, and second, reduction in the number of match candidates, promising performance improvements. Previous work on scope, however, has focused on applying it to rule hierarchies, which reduces the number of matches performed, but not necessarily the cost of finding a single match. In this paper we define and explore a hierarchical scope formalism applied to the input graph, with associated modifications to the transformation rule definition. We then experimentally evaluate the benefits and challenges of our scoped model transformations in the state-of-the-art graph rewriting tool GrGen and our research-oriented, meta-modeling and rule-based model transformation tool AToMPM. We use a non-trivial “fire spreading” simulation transformation taken from distributed simulation community and a mutual exclusion transformation benchmark to demonstrate that integration of scope results in an elegant, intuitive, and efficient way of solving model transformation problems.