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.
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