Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

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Principles of Model Checking

N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

This paper describes a major new release of the PRISMprobabilistic model checker, adding, in particular, quantitative verification of (priced) probabilistic timed automata. These model systems exhibiting probabilistic, nondeterministic and real-time characteristics. In many application domains, all three aspects are essential; this includes, for example, embedded controllers in automotive or avionic systems, wireless communication protocols such as Bluetooth or Zigbee, and randomised security protocols. PRISM, which is open-source, also contains several new components that are of independent use. These include: an extensible toolkit for building, verifying and refining abstractions of probabilistic models; an explicit-state probabilistic model checking library; a discrete-event simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite.

http://www.prismmodelchecker.org/papers/cav11.pdf

PRISM 4.0: verification of probabilistic real-time systems

This paper presents the overal structure, the design criteria, and the main features of the tool box Uppaal. It gives a detailed user guide which describes how to use the various tools of Uppaal version 2.02 to construct abstract models of a real-time system, to simulate its dynamical behavior, to specify and verify its safety and bounded liveness properties in terms of its model. In addition, the paper also provides a short review on case-studies where Uppaal is applied, as well as references to its theoretical foundation.

UPPAAL in a Nutshell

Real and Complex Analysis

This paper is focused on adapting symmetry reduction, a technique that is highly successful in traditional model checking, to stochastic hybrid systems. We first show that performability analysis of stochastic hybrid systems can be reduced to a stochastic reachability analysis (SRA). Then, we generalize the notion of symmetry reduction as recently proposed for probabilistic model checking, to continuous probabilistic systems. We provide a rigorous mathematical foundation for the reduction technique in the continuous case and also investigate its observability perspective. For stochastic hybrid systems, characterizations for this reduction technique are provided, in terms of their infinitesimal generator.

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Symmetry reduction for stochastic hybrid systems

Boolean-logic driven Markov processes (BDMPs) is a graphical language for reliability analysis of dynamic repairable systems. BDMPs are capable of defining complex interdependencies among failure modes such as functional dependencies and state-dependent failures. The interpretation of BDMPs is non-trivial due to the many possible complex interactions of activation and failure mechanisms. This paper presents a formal semantics of repairable BDMPs by using generalized stochastic Petri nets (GSPNs). Our semantics is modular and thus easily extendable to other elements, e.g., leaves dedicated to security applications. Priorities on GSPN transitions are used to impose a partial order on various possible interleaving of activation and failure mechanisms. The semantics is realized by the prototypical tool BDMP2GSPN that converts a Figaro description of a BDMP into a GSPN. The reliability and availability metrics of BDMPs are obtained using the probabilistic model-checking capability of the existing GreatSPN tool. Experiments show that our GSPN semantics corresponds to the BDMP interpretation by the tool yet another Monte Carlo simulator (YAMS).

Explaining Boolean-Logic Driven Markov Processes using GSPNs

This paper presents an abstraction-refinement framework for Segala’s probabilistic automata (PA), a slight variant of Markov decision processes. We use Condon and Ladner’s two-player probabilistic game automata extended with possible and required transitions—as in Larsen and Thomsen’s modal transition systems—as abstract models. The key idea is to refine player-one and player-two states separately resulting in a nested abstract-refine loop. We show the adequacy of this approach for obtaining tight bounds on extremal reachability probabilities.

Modal Stochastic Games

This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.

Bayesian Inference by Symbolic Model Checking

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Joost-Pieter Katoen

Papers

Symmetry reduction for stochastic hybrid systems

Explaining Boolean-Logic Driven Markov Processes using GSPNs

Modal Stochastic Games

Bayesian Inference by Symbolic Model Checking

Synthesizing optimal bias in randomized self-stabilization