Towards a Model-Based DevOps for Cyber-Physical Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12055)


The emerging field of Cyber-Physical Systems (CPS) calls for new scenarios of the use of models. In particular, CPS require to support both the integration of physical and cyber parts in innovative complex systems or production chains, together with the management of the data gathered from the environment to drive dynamic reconfiguration at runtime or finding improved designs. In such a context, the engineering of CPS must rely on models to uniformly reason about various heterogeneous concerns all along the system life cycle. In the last decades, the use of models has been intensively investigated both at design time for driving the development of complex systems, and at runtime as a reasoning layer to support deployment, monitoring and runtime adaptations. However, the approaches remain mostly independent. With the advent of DevOps principles, the engineering of CPS would benefit from supporting a smooth continuum of models from design to runtime, and vice versa. In this vision paper, we introduce a vision for supporting model-based DevOps practices, and we infer the corresponding research roadmap for the modeling community to address this vision by discussing a CPS demonstrator.



This work has been partially supported and funded by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, by the FWF under the grant numbers P28519-N31 and P30525-N31, and the Inria/Safran collaboration GLOSE.


  1. 1.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Human-Computer Interaction Series. Springer, London (2011). Scholar
  2. 2.
    Broy, M., Schmidt, A.: Challenges in engineering cyber-physical systems. Computer 47(2), 70–72 (2014) CrossRefGoogle Scholar
  3. 3.
    Estefan, J.: Survey of model-based systems engineering (MBSE) methodologies. INCOSE MBSE Focus Group 1–47 (2007)Google Scholar
  4. 4.
    García, J., Cabot, J.: Stepwise adoption of continuous delivery in model-driven engineering. In: Proceedings of DEVOPS (2018)Google Scholar
  5. 5.
    Gruhn, V., Schäfer, C.: BizDevOps: because DevOps is not the end of the story. In: Fujita, H., Guizzi, G. (eds.) SoMeT 2015. CCIS, vol. 532, pp. 388–398. Springer, Cham (2015). Scholar
  6. 6.
    Lee, E.A.: Cyber physical systems: design challenges. In: Proceedings of the 11th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369 (2008)Google Scholar
  7. 7.
    Mazak, A., Wimmer, M.: Towards liquid models: an evolutionary modeling approach. In: Proceedings of the 18th IEEE Conference on Business Informatics (CBI), pp. 104–112 (2016).
  8. 8.
    Tendeloo, Y.V., Mierlo, S.V., Vangheluwe, H.: A multi-paradigm modelling approach to live modelling. Softw. Syst. Model. 18(5), 2821–2842 (2019). Scholar
  9. 9.
    Vangheluwe, H., et al.: MPM4CPS: multi-paradigm modelling for cyber-physical systems. In: Proceedings of the Project Showcase @ STAF 2015, pp. 1–10 (2016)Google Scholar
  10. 10.
    Weghofer, S.: Moola - a Groovy-based model operation orchestration language. Master’s thesis, TU Wien (2017)Google Scholar
  11. 11.
    Whittle, J., Hutchinson, J., Rouncefield, M.: The state of practice in model-driven engineering. IEEE Software 31(3), 79–85 (2014)CrossRefGoogle Scholar
  12. 12.
    Wolny, S., Mazak, A., Wimmer, M., Konlechner, R., Kappel, G.: Model-driven time-series analytics. Enterp. Model. Inf. Syst. Archit. 13(Special), 252–261 (2018). Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University Toulouse & InriaRennesFrance
  2. 2.Johannes Kepler University Linz & CDL-MINTLinzAustria

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