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SMC4PEP: Stochastic Model Checking of Product Engineering Processes

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13241)

Abstract

Product Engineering Processes (PEPs) are used for describing complex product developments in big enterprises such as automotive and avionics industries. The Business Process Model Notation (BPMN) is a widely used language to encode interactions among several participants in such PEPs. In this paper, we present SMC4PEPl as a tool to convert graphical representations of a business process using the BPMN standard to an equivalent discrete-time stochastic control process called Markov Decision Process (MDP). To this aim, we first follow the approach described in an earlier investigation to generate a semantically equivalent business process which is more capable of handling the PEP complexity. In particular, the interaction between different levels of abstraction is realized by events rather than direct message flows. Afterwards, SMC4PEPl converts the generated process to an MDP model described by the syntax of the probabilistic model checking tool PRISM. As such, SMC4PEPl provides a framework for automatic verification and validation of business processes in particular with respect to requirements from legal standards such as Automotive SPICE. Moreover, our experimental results confirm a faster verification routine due to smaller MDP models generated from the alternative event-based BPMN models.

Keywords

  • Product Engineering Processes
  • Verification and validation
  • Probabilistic model checking
  • Markov decision processes
  • Probabilistic reward CTL

References

  1. Daclin, N., Vallespir, B., Vincent, C.: Enabling model checking for collaborative process analysis: from bpmn to ‘network of timed automata’. In: Enterprise Inforation Systems. vol. 9, pp. 279–299. Taylor and Francis (2015)

    Google Scholar 

  2. Dehnert, C., Junges, S., Katoen, J., Volk, M.: The probabilistic model checker storm (extended abstract). CoRR abs/1610.08713 (2016), http://arxiv.org/abs/1610.08713

  3. Duran, F., Rocha, C., Salaün, G.: Stochastic analysis of bpmn with time in rewriting logic. In: Science of Computer Programming. pp. 168, pp. 1–17. Elsevier (2018)

    Google Scholar 

  4. Europe, S.S.C.: Enterprise Architect 15.2 [Software] (2021), https://www.sparxsystems.de

  5. Gausemeier, J., Dumitrescu, R., Steffen, D., Czaja, A., Wiederkehr, O., Tschirner, C.: Systems engineering in der industriellen praxis. Heinz Nixdorf Institut, Frauenhofer Institut, UNITY AG (2013)

    Google Scholar 

  6. Gebler, D., Hashemi, V., Turrini, A.: Computing behavioral relations for probabilistic concurrent systems. In: ROCKS 2012. pp. 117–155. Springer Berlin Heidelberg, Berlin, Heidelberg (2014)

    Google Scholar 

  7. Group, O.O.M.: Business process model and notation (bpmn). Website (2014), https://www.omg.org/spec/BPMN

  8. Hage, H., Hashemi, V., Mantwill, F.: Towards a systems engineering based automotive product engineering process. In: Software Architecture - 14th European Conference. Communications in Computer and Information Science, vol. 1269, pp. 527–541. Springer (2020)

    Google Scholar 

  9. Hahn, E.M., Hashemi, V., Hermanns, H., Turrini, A.: Exploiting robust optimization for interval probabilistic bisimulation. In: Agha, G., Van Houdt, B. (eds.) Quantitative Evaluation of Systems. pp. 55–71. Springer International Publishing, Cham (2016)

    Google Scholar 

  10. Hahn, E.M., Hermanns, H., Wachter, B., Zhang, L.: Param: A model checker for parametric markov models. In: CAV. pp. 660–664 (2010)

    Google Scholar 

  11. Hahn, E.M., Li, Y., Schewe, S., Turrini, A., Zhang, L.: iscas m c: a web-based probabilistic model checker. In: International Symposium on Formal Methods. pp. 312–317. Springer (2014)

    Google Scholar 

  12. Hartmanns, A., Hermanns, H.: The modest toolset: An integrated environment for quantitative modelling and verification. In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems. pp. 593–598. Springer (2014)

    Google Scholar 

  13. Hashemi, V., Hermanns, H., Song, L., Subramani, K., Turrini, A., Wojciechowski, P.: Compositional bisimulation minimization for interval markov decision processes. In: Language and Automata Theory and Applications. pp. 114–126. Springer (2016)

    Google Scholar 

  14. Hebert, L.: Specification, verification and optimisation of business process. Technical University of Denmark (2014)

    Google Scholar 

  15. Kwiatkowska, M., Norman, G., Parker, D.: Probabilistic symbolic model checking with prism: A hybrid approach. In: Katoen, J.P., Stevens, P. (eds.) Tools and Algorithms for the Construction and Analysis of Systems. pp. 52–66. Springer Berlin Heidelberg, Berlin, Heidelberg (2002)

    Google Scholar 

  16. Lam, V.S.W.: Formal analysis of BPMN models: a nusmv-based approach. Int. J. Softw. Eng. Knowl. Eng. 20(7), 987–1023 (2010), https://doi.org/10.1142/S0218194010005079

  17. Martin Glinz, S.F.: Software quality selected chapter, chapter 7, process quality. University of Zürich, Institut for Informatics (2007)

    Google Scholar 

  18. Mendoza Morales, L.: Business process verification: The application of model checking and timed automata. CLEI Electronic Journal 17,  3–3 (08 2014)

    Google Scholar 

  19. Ou-Yang, C., Lin, Y.D.: BPMN-based business process model feasibility analysis: a Petri net approach. vol. 46, pp. 3763–3781. Taylor and Francis (2008), https://doi.org/10.1080/00207540701199677

  20. Parker, D.: Lecture 14 model checking for MDPs. University of Oxford, Department Science (2011)

    Google Scholar 

  21. SIG, V.Q.W.G...A.: Automotive SPICE Process Assessment/Reference Model. Automotive SPICE (2017)

    Google Scholar 

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Hage, H., Seferis, E., Hashemi, V., Mantwill, F. (2022). SMC4PEP: Stochastic Model Checking of Product Engineering Processes. In: Johnsen, E.B., Wimmer, M. (eds) Fundamental Approaches to Software Engineering. FASE 2022. Lecture Notes in Computer Science, vol 13241. Springer, Cham. https://doi.org/10.1007/978-3-030-99429-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-99429-7_9

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