Reducing the Verbosity of Imperative Model Refinements by Using General-Purpose Language Facilities

摘要

Refinements are model transformations that leave large parts of the source models unchanged. Therefore, if refinements are executed outplace, model elements need to be copied to the target model. Refinements written in imperative languages are increasingly verbose, unless suitable language facilities exist for creating these copies implicitly. Thus, for languages restricted to general-purpose facilities, the verbosity of refinements is still an open problem. Existing approaches towards reducing this verbosity suffer from the complexity of developing a higher-order transformation to synthesize the copying code. In this paper, we propose a generic transformation library for creating implicit copies, reducing the verbosity without a higher-order transformation. We identify the underlying general-purpose language facilities, and compare state-of-the-art languages against these requirements. We give a proof of concept using the imperative QVTo language, and showcase the ability of our library to reduce the verbosity of an industrial-scale transformation chain.