Statistical Model Checking of Incomplete Stochastic Systems

Abstract : We study incomplete stochastic systems that are missing some parts of their design, or are lacking information about some components. It is interesting to get early analysis results of the requirements of these systems, in order to adequately refine their design. In previous works, models for incomplete systems are analysed using model checking techniques for three-valued temporal logics. In this paper, we propose statistical model checking algorithms for these logics. We illustrate our approach on a case-study of a network system that is refined after the analysis of early designs.
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Communication dans un congrès
ISoLA 2018 - International Symposium on Leveraging Applications of Formal Methods, Nov 2018, Limassol, Cyprus. Springer, 11245, pp.354-371, 2018, LNCS. 〈10.1007/978-3-030-03421-4_23〉
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Contributeur : Tania Richmond <>
Soumis le : vendredi 8 février 2019 - 09:49:56
Dernière modification le : samedi 9 février 2019 - 01:21:08

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Shiraj Arora, Axel Legay, Tania Richmond, Louis-Marie Traonouez. Statistical Model Checking of Incomplete Stochastic Systems. ISoLA 2018 - International Symposium on Leveraging Applications of Formal Methods, Nov 2018, Limassol, Cyprus. Springer, 11245, pp.354-371, 2018, LNCS. 〈10.1007/978-3-030-03421-4_23〉. 〈hal-02011309〉

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