Giving a Model-Based Testing Language a Formal Semantics via Partial MAX-SAT

Abstract

Domain-specific Languages (DSLs) are widely used in modelbased testing to make the benefits of modeling available to test engineers while avoiding the problem of excessive learning effort. Complex DSLs benefit from a formal definition of their semantics for model processing as well as consistency checking. A formal semantics can be established by mapping the model domain to a well-known formalism. In this paper, we present an industrial use case which includes a mapping from domainspecific models to Moore Machines, based on a Partial MAX-SAT problem, encoding a predicative semantics for the model-to-model mapping. We show how Partial MAX-SAT solves the frame problem for a nontrivial DSL in which the non-effect on variables cannot be determined statically. We evaluated the performance of our model-transformation algorithm based on models from our industrial use case.