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

Approximate computing is a computational paradigm in which the requirement of having fully deterministic and accurate arithmetical operations is dropped [4]. For diverse practical problem (e.g. video decoding or image compression) by relaxing this requirement one can obtain considerably more efficient algorithms—be it in terms of execution time, memory usage, energy consumption, etc. When moving from accurate to approximate computations, a natural question arises: How accurate is the result of the approximate computation compared to the result of the accurate one? Any attempt to seriously answer this question raises the need for an appropriate formal model of approximate computations. Probabilistic programs extend conventional programs with probabilistic choices or random assignments and indeed provide a well–studied and suitable model for this purpose. Their syntax and semantics [5, 6, 3, 2] are flexible and expressive enough to model many aspects of approximate computing quite naturally. A recent approach for dealing with quantitative reliability analysis of approximate computations brought up the concept of unreliable operators [1]. In this approach instead of performing e.g. the accurate assignment x = x + y, one performs the approximate assignment x = x +. y, the latter being specified through a reliability value 0 ≤ e ≤ 1. Then with probability 1−e the approximate assignment yields the expected effect, while with probability e it produces an arbitrary—and thus erroneous—effect. This model, however, is unable to quantify how much the erroneous effect departs from the accurate one. Our goal is to take this reliability analysis a step further by employing a more expressive language of probabilistic programs: the so-called probabilistic guarded command language (pGCL) [6]—an extension of Dijkstra’s guarded command language with probabilistic choices. By employing this language besides representing the probability with wich erroneous computation occurs, we can provide more stochastic information such as the most likely effect or the expected (i.e. average) effect of the erroneous computations. For instance we can model an approximate assignment as a pGCL program

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Probabilistic Programs - A Natural Model for Approximate Computations

Finding an ε-close Variation of Parameters in Bayesian Networks

We describe the Amber tool for proving and refuting the termination of a class of probabilistic while-programs with polynomial arithmetic, in a fully automated manner. Amber combines martingale theory with properties of asymptotic bounding functions and implements relaxed versions of existing probabilistic termination proof rules to prove/disprove (positive) almost sure termination of probabilistic loops. Amber supports programs parameterized by symbolic constants and drawing from common probability distributions. Our experimental comparisons give practical evidence of Amber outperforming existing state-of-the-art tools.

The Probabilistic Termination Tool Amber.

This special section is devoted to a selection of journal versions of papers that were presented at the first International Symposium On Leveraging Applications of Formal Methods. All papers are relevant to the field of quantitative systems validation. Three papers report on applying state-of-the-art techniques to (industrial) case studies, whereas one paper addresses the evaluation of networks in building automation.

Joost-Pieter Katoen

Papers

Probabilistic Programs - A Natural Model for Approximate Computations

Finding an ε-close Variation of Parameters in Bayesian Networks

The Probabilistic Termination Tool Amber.

Guest editors’ introduction: quantitative analysis of real-time embedded systems

Probably on Time and within Budget: On Reachability in Priced Probabilistic Timed Automata