Two Decades of Probabilistic Verification —Reflections and Perspectives

12 - 16 November 2007

Venue: Lorentz Center@Oort

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Model checking is emerging as a practical tool for automated debugging of complex reactive systems such as embedded controllers and network protocols. In model checking, specifications about the system are expressed as (temporal) logic formulas, and efficient symbolic algorithms are used to traverse the model defined by the system and check if the specification holds or not. Extremely large state-spaces can often be traversed in minutes.

Whereas traditional model checking focuses on the absolute correctness of systems, in practice such rigid notions are hard, or even impossible, to guarantee. Instead, systems are subject to various phenomena of stochastic nature, such as message loss or garbling, unpredictable environments, faults, and delays. Correctness thus is of a less absolute nature. Accordingly, instead of checking whether system failures are impossible a more realistic aim is to establish, for instance, whether "the chance of shutdown occurring is at most 0.01%". Similarly, the question whether a distributed computation terminates becomes "will it eventually terminate with probability 1?". These queries can be checked using stochastic model checking, an automated technique for validating stochastic models.

Stochastic model checking is based on conventional model checking, since it relies on reachability analysis of the underlying transition system, but must also entail the calculation of the actual likelihoods through appropriate numerical methods. In addition to the qualitative statements made by conventional model checking, this provides the possibility to make quantitative statements about the system. Stochastic model checking uses extensions of temporal logics with probabilistic operators, affording the expression of these quantitative statements. Stochastic model checking is typically based on discrete-time and continuous-time Markov chains (DTMCs and CTMCs, respectively), or Markov decision processes (MDPs). Whereas Markov chains are fully stochastic, MDPs allow for nondeterminism. The former models are intensively used in performance and dependability analysis, whereas MDPs are of major importance in stochastic operations research and automated planning in AI. Extensions of model checking to stochastic models originate from the mid 1980s, first focusing on qualitative properties where the probability bounds are restricted to 0 or 1, but later also considering quantitative properties. During the last decade, these methods have been extended, refined and improved, and - most importantly - been supported by software tools. Currently, model checkers for DTMCs, CTMCs and MDPs do exist, and have been applied to several case studies ranging from randomized distributed algorithms to dependability analysis of workstation clusters. With the currently available technology, models of 10^6 - 10^7 states can be successfully checked.

In December 2002, the VOSS project consortium organized a successful Dagstuhl seminar on Validation of Stochastic Systems aimed at young(er) people, where themes were assigned to be worked upon and presented on by the participants. In the years following the Dagstuhl seminar, there have been major developments in the field, and therefore the organizers believe the time is there to organize a successor workshop to survey again state-of-the-art techniques for the modeling and verification of random aspects of computer systems, but also to identify the main research challenges for the next decade.


    Markus Siegle  

    Holger Hermanns  

    Cristel Baier  

    Joost-Pieter Katoen  

    Boudewijn Haverkort  

    Frits Vaandrager  

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