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This tutorial paper surveys the main features of Uppaal SMC, a model checking approach in Uppaal family that allows us to reason on networks of complex real-timed systems with a stochastic semantic. We demonstrate the modeling features of the tool, new verification algorithms and ways of applying them to potentially complex case studies.

International audienceQuantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties. Another approach to solve the model checking problem is to simulate the system for finitely many executions, and use hypothesis testing to infer whether the samples provide a statistical evidence for the satisfaction or violation of the specification. In this tutorial, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity

Abstract.Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than state-of-the-art techniques, even when no prior knowledge is available.

We study the problem of model checking software product line (SPL) behaviours against temporal properties. This is more difficult than for single systems because an SPL with n features yields up to 2 n individual systems to verify. As each individual verification suffers from state explosion, it is crucial to propose efficient formalisms and heuristics.We recently proposed featured transition systems (FTS), a compact representation for SPL behaviour, and defined algorithms for model checking FTS against linear temporal properties. Although they showed to outperform individual system verifications, they still face a state explosion problem as they enumerate and visit system states one by one.In this paper, we tackle this latter problem by using symbolic representations of the state space. This lead us to consider computation tree logic (CTL) which is supported by the industry-strength symbolic model checker NuSMV. We first lay the foundations for symbolic SPL model checking by defining a feature-oriented version of CTL and its dedicated algorithms. We then describe an implementation that adapts the NuSMV language and tool infrastructure. Finally, we propose theoretical and empirical evaluations of our results. The benchmarks show that for certain properties, our algorithm is over a hundred times faster than model checking each system with the standard algorithm.

We study the relation between specifications of component behaviors and contracts providing means to specify assumptions on environments as well as component guarantees. We show how a contract framework can be built in a generic way on top of any specification theory which supports composition and specification refinement. Our contract framework lifts refinement to the level of contracts and proposes a notion of contract composition on the basis of dominating contracts. Contract composition satisfies a universal property and can be constructively defined if the underlying specification theory is complete, i.e. it offers operators for quotienting and conjoining specifications. We illustrate our generic construction of contracts by moving a specification theory for modal transition systems to contracts and we show that a (previously proposed) trace-based contract theory is an instance of our framework.

Abstract. To detect hard-to-find concurrency bugs, current tools systematically explore all possible interleavings of the transitions in a program. Unfortunately, concurrent programs have a large number of possible interleavings due to nondeterminism. Speeding up such tools requires pruning the state space explored. Partial-order reduction (POR) techniques can substantially prune the number of explored interleavings. These techniques require defining a dependency relation on transitions in the program, and exploit independency among certain transitions to prune the state space. We observe that actor systems, a prevalent class of programs where computation entities communicate by exchanging messages, exhibit a dependency relation among co-enabled transitions with an interesting property: transitivity. This paper introduces a novel dynamic POR technique, TransDPOR, that exploits the transitivity of the dependency relation in actor systems. Empirical results show that leveraging transitivity speeds up exploration by up to two orders of magnitude compared to existing POR techniques.

This paper offers a natural stochastic semantics of Networks of Priced Timed Automata (NPTA) based on races between components. The semantics provides the basis for satisfaction of Probabilistic Weighted CTL properties (PWCTL), conservatively extending the classical satisfaction of timed automata with respect to TCTL. In particular the extension allows for hard real-time properties of timed automata expressible in TCTL to be refined by performance properties, e.g. in terms of probabilistic guarantees of time-and cost-bounded properties. A second contribution of the paper is the application of Statistical Model Checking (SMC) to efficiently estimate the correctness of non-nested PWCTL model checking problems with a desired level of confidence, based on a number of independent runs of the NPTA. In addition to applying classical SMC algorithms, we also offer an extension that allows to efficiently compare performance properties of NPTAs in a parametric setting. The third contribution is an efficient tool implementation of our result and applications to several case studies.