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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Variability Identification and Representation for Automotive Simulink Models


    Contributors:


    Publication date :

    2019-01-01


    Size :

    31 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Evaluation of Variability Concepts for Simulink in the Automotive Domain

    Kolassa, Carsten / Rendel, Holger / Rumpe, Bernhard | British Library Conference Proceedings | 2015




    Automotive powertrain co-simulation with Modelica and Simulink

    Zaimin Zhong, / Bonian Wu, / Xueping Chen, et al. | IEEE | 2014