Abstract
This chapter presents an automated framework for identifying and representing different types of variability in Simulink models. The framework is based on the observed variants found in similar subsystem patterns inferred using Simone, a model clone detection tool, and an empirically derived set of variability operators for Simulink models. We demonstrate the application of these operators to six example systems, including automotive systems, using two alternative variation analysis techniques, one text-based and one graph-based, and show how we can represent the variation in each of the similar subsystem patterns as a single subsystem template directly in the Simulink environment. The product of our framework is a single consolidated subsystem model capable of expressing the observed variability across all instances of each inferred pattern. The process of pattern inference and variability analysis is largely automated and can be easily applied to other collections of Simulink models. We provide tool support for the variability identification and representation using the graph-based approach.
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Acknowledgements
This work is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada as part of the NECSIS Automotive Partnership with General Motors, IBM Canada, and Malina Software Corp. and by an Ontario Research Fund Research Excellence grant.
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Alalfi, M.H., Rapos, E.J., Stevenson, A., Stephan, M., Dean, T.R., Cordy, J.R. (2019). Variability Identification and Representation for Automotive Simulink Models. In: Dajsuren, Y., van den Brand, M. (eds) Automotive Systems and Software Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-12157-0_6
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