Variability-based model transformation: formal foundation and application

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 introduce variability-based model transformation . The key idea is to encode a set of similar rules into a compact representation, called variability-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.