Model-checking is an effective formal verification technique that has also been extended to quantitative logics and models such as PCTL and DTMCs as well as CSL and CTMCs/CTMDPs. Unfortunately, the state-space explosion problem of classical model-checking algorithms affects also quantitative extensions. Mean-field techniques provide approximations of the mean behaviour of large population models. These approximations are deterministic: a unique value of the fractions of agents in each state is computed for each time instant. A drastic reduction of the size of the model is obtained enabling the definition of an efficient model-checking algorithm. This paper is a survey of work we have done in the last few years in the area of mean-field approximated probabilistic model-checking. We start with a brief description of FlyFast, an on-the-fly model checker we have developed for approximated bounded PCTL model-checking, based on mean-field population DTMC approximation. Then we show an example of use of FlyFast in the context of Collective Adaptive Systems. We also discuss two additional interesting front-ends for FlyFast; the first one is a translation from CTMC-based population models and (a fragment of) CSL that allows for approximate probabilistic model-checking in the continuous stochastic time setting; the second one is a translation from a predicate-based process interaction language that allows for probabilistic model-checking of models based on components equipped both with behaviour and with attributes, on which predicates are defined that can be used in component interaction primitives.

FlyFast: A Scalable Approach to Probabilistic Model-Checking Based on Mean-Field Approximation

Michele Loreti;
2017

Abstract

Model-checking is an effective formal verification technique that has also been extended to quantitative logics and models such as PCTL and DTMCs as well as CSL and CTMCs/CTMDPs. Unfortunately, the state-space explosion problem of classical model-checking algorithms affects also quantitative extensions. Mean-field techniques provide approximations of the mean behaviour of large population models. These approximations are deterministic: a unique value of the fractions of agents in each state is computed for each time instant. A drastic reduction of the size of the model is obtained enabling the definition of an efficient model-checking algorithm. This paper is a survey of work we have done in the last few years in the area of mean-field approximated probabilistic model-checking. We start with a brief description of FlyFast, an on-the-fly model checker we have developed for approximated bounded PCTL model-checking, based on mean-field population DTMC approximation. Then we show an example of use of FlyFast in the context of Collective Adaptive Systems. We also discuss two additional interesting front-ends for FlyFast; the first one is a translation from CTMC-based population models and (a fragment of) CSL that allows for approximate probabilistic model-checking in the continuous stochastic time setting; the second one is a translation from a predicate-based process interaction language that allows for probabilistic model-checking of models based on components equipped both with behaviour and with attributes, on which predicates are defined that can be used in component interaction primitives.
978-3-319-68269-3
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11581/405860
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