Constraint-driven modeling through transformation

Abstract

In model-driven software engineering, model transformation plays a key role for automatically generating and updating models. Transformation rules define how source model elements are to be transformed into target model elements. However, defining transformation rules is a complex task, especially in situations where semantic differences or incompleteness allow for alternative interpretations or where models change continuously before and after transformation. This paper proposes constraint-driven modeling where transformation is used to generate constraints on the target model rather than the target model itself. We evaluated the approach on three case studies that address the above difficulties and other common transformation issues. We also developed a proof-of-concept implementation that demonstrates its feasibility. The implementation suggests that constraint-driven transformation is an efficient and scalable alternative and/or complement to traditional transformation.