%0 Conference Proceedings
%T Giving a Model-Based Testing Language a Formal Semantics via Partial MAX-SAT
%+ Graz University of Technology [Graz] (TU Graz)
%+ AVL List GmbH
%A Aichernig, Bernhard, K.
%A Burghard, Christian
%Z Part 1: Model-Based Testing
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 32th IFIP International Conference on Testing Software and Systems (ICTSS)
%C Naples, Italy
%Y Valentina Casola
%Y Alessandra De Benedictis
%Y Massimiliano Rak
%I Springer International Publishing
%3 Testing Software and Systems
%V LNCS-12543
%P 35-51
%8 2020-12-09
%D 2020
%R 10.1007/978-3-030-64881-7_3
%K Partial moore machines
%K Model transformation
%K Consistency checking
%K Formal semantics
%K Frame problem
%K Partial MAX-SAT
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Domain-specific Languages (DSLs) are widely used in model-based 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 domain-specific 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 non-trivial 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.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-03239819/document
%2 https://inria.hal.science/hal-03239819/file/497758_1_En_3_Chapter.pdf
%L hal-03239819
%U https://inria.hal.science/hal-03239819
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-ICTSS
%~ IFIP-LNCS-12543