M. Wimmer, P. Novak, R. Sindelar, L. Berardinelli, T. Mayerhofer, A. Mazak: Cardinality-Based Variability Modeling with AutomationML, ETFA 2017, Limassol, Cyprus; 12.09.2017 - 15.09.2017; in Proceedings of ETFA 2017, IEEE, (2017), ISBN: 978-1-5090-6505-9, pages 1 - 4. doi: 10.1109/ETFA.2017.8247711

Variability modeling is an emerging topic in the general field of systems engineering and, with current trends such as Industrie 4.0, it gains more and more interest in the domain of production systems. Therefore, it is not sufficient to describe systems in several specific cases, but instead families of systems have to be used. In this paper we introduce a role class library for AutomationML to explicitly represent variability. This allows to exchange not only system descriptions but also system family descriptions. We argue for a light-weight extension of AutomationML. The variability-based modeling approach is based on cardinalities, which is a well-known concept from conceptual modeling and feature modeling. Furthermore, we also show how instantiations of variability models can be validated by our EMF-based AutomationML workbench.

Cardinality-Based Variability Modeling with AutomationML