Variability-based model transformation: formal foundation and application
Daniel Strüber, Julia Rubin, Thorsten Arendt, Marsha Chećhik, Gabriele Taentzer, Jennifer Plöger · Formal Aspects of Computing · 2017
Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introducevariability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, calledvariability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact.