MoDeST

MoDeST --- a modelling and description language for stochastic timed systems. This paper presents a modelling language, called MoDeST, for describing the behaviour of discrete event systems. The language combines conventional programming constructs - such as iteration, alternatives, atomic statements, and exception handling - with means to describe complex systems in a compositional manner. In addition, MoDeST incorporates means to describe important phenomena such as non-determinism, probabilistic branching, and hard real-time as well as soft real-time (i.e., stochastic) aspects. The language is influenced by popular and user-friendly specification languages such as Promela, and deals with compositionality in a light-weight process-algebra style. Thus, MoDeST (i) covers a very broad spectrum of modelling concepts, (ii) possesses a rigid, process-algebra style semantics, and (iii) yet provides modern and flexible specification constructs


References in zbMATH (referenced in 32 articles , 1 standard article )

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  1. Lavaei, Abolfazl; Khaled, Mahmoud; Soudjani, Sadegh; Zamani, Majid: AMYTISS: a parallelized tool on automated controller synthesis for large-scale stochastic systems (2020)
  2. Cauchi, Nathalie; Abate, Alessandro: Poster abstract: StocHy -- automated verification and synthesis of stochastic processes. (2019)
  3. Baier, Christel; de Alfaro, Luca; Forejt, Vojtěch; Kwiatkowska, Marta: Model checking probabilistic systems (2018)
  4. Bouyer, Patricia; Brihaye, Thomas; Carlier, Pierre; Menet, Quentin: Compositional design of stochastic timed automata (2016)
  5. Garavel, Hubert: Revisiting sequential composition in process calculi (2015)
  6. Latella, Diego; Massink, Mieke; de Vink, Erik P.: Bisimulation of labelled state-to-function transition systems coalgebraically (2015)
  7. Gómez, Rodolfo: Model-checking timed automata with deadlines with Uppaal (2013)
  8. Hahn, Ernst Moritz; Hartmanns, Arnd; Hermanns, Holger; Katoen, Joost-Pieter: A compositional modelling and analysis framework for stochastic hybrid systems (2013)
  9. Norman, Gethin; Parker, David; Sproston, Jeremy: Model checking for probabilistic timed automata (2013)
  10. D’Argenio, Pedro R.; Sánchez Terraf, Pedro; Wolovick, Nicolás: Bisimulations for non-deterministic labelled Markov processes (2012)
  11. Katoen, Joost-Pieter; van de Pol, Jaco; Stoelinga, Mariëlle; Timmer, Mark: A linear process-algebraic format with data for probabilistic automata (2012)
  12. Markovski, J.; D’argenio, P. R.; Baeten, J. C. M.; de Vink, E. P.: Reconciling real and stochastic time: the need for probabilistic refinement (2012)
  13. Fietzke, Arnaud; Hermanns, Holger; Weidenbach, Christoph: Superposition-based analysis of first-order probabilistic timed automata (2010)
  14. Hillston, Jane: Process algebras for collective dynamics. (Extended abstract) (2010) ioport
  15. Mader, Angelika; Bohnenkamp, Henrik; Usenko, Yaroslav S.; Jansen, David N.; Hurink, Johann; Hermanns, Holger: Synthesis and stochastic assessment of cost-optimal schedules (2010) ioport
  16. van Beek, D. A.; Cuijpers, P. J. L.; Markovski, J.; Nadales Agut, D. E.; Rooda, J. E.: Reconciling urgency and variable abstraction in a hybrid compositional setting (2010)
  17. Gómez, Rodolfo: A compositional translation of timed automata with deadlines to Uppaal timed automata (2009)
  18. Markovski, Jasen; de Vink, Erik P.: Performance evaluation of distributed systems based on a discrete real- and stochastic-time process algebra (2009)
  19. Xu, TianHua; Tang, Tao; Gao, ChunHai; Cai, BaiGen: Dependability analysis of the data communication system in train control system (2009)
  20. Markovski, Jasen; de Vink, Erik P.: Extending timed process algebra with discrete stochastic time (2008)

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Further publications can be found at: https://depend.cs.uni-sb.de/publications/index.php