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Statistical Model Checking (SMC) is a trade-off between testing and formal verification. The core idea of the approach is to conduct some simulations of the system and verify if they satisfy some given property. In this paper we show that SMC is easily parallelizable on a master/slaves architecture by introducing a series of algorithms that scale almost linearly with respect to the number of slave computers. Our approach has been implemented in the UPPAAL SMC toolset and applied on non-trivial case studies.
Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae
We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined
Parametric Markov chains occur quite naturally in various applications: they can be used for a conservative analysis of probabilistic systems (no matter how the parameter is chosen, the system works to specification); they can be used to find optimal
Cloud-based infrastructure has been increasingly adopted by the industry in distributed software development (DSD) environments. Its proponents claim that its several benefits include reduced cost, increased speed and greater productivity in software
Runtime verification is a computing analysis paradigm based on observing a system at runtime (to check its expected behaviour) by means of monitors generated from formal specifications. Distributed runtime verification is runtime verification in conn