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

Some twentyfive years ago, the field of computer-communication system performance evaluation and the field of formal specification and verification were regarded as completely disjunct. The former field focussed on the quantitative aspects of system behaviour, expressed in measures such as delays, throughputs and loss probabilities, whereas the latter field focussed on the qualitative aspects of system behaviour, expressed in measures (or, properties) such as system liveness, deadlock freeness and safety. Over the years, however, this distinction has shown to be not always useful. In fact, we see a large variety of systems for which the qualitative behaviour cannot be decoupled from the quantitative aspect. Think for instance of communication protocols in an embedded system setting: the qualitative correctness of a protocol, without considering (absolute) timing aspects, is not enough for classifying a protocol as correct. Indeed, only when the protocol behaves as it should, and does so in a timely manner, the protocol can be regarded as correct. Observations of this kind have lead to a variety of integrated approaches toward performance evaluation and verification.

Performance and verification

This paper introduces the model-based software development methodology SMA--the Smyle Modeling Approach--which is centered around Smyle, a dedicated learning procedure to support engineers to interactively obtain design models from requirements, characterized as either being desired (positive) or unwanted (negative) system behavior. The learning approach is complemented by scenario patterns where the engineer can specify clearly desired or unwanted behavior. This way, user interaction is reduced to the interesting scenarios limiting the design effort considerably. In SMA, the learning phase is complemented by an effective analysis phase that allows for detecting design flaws at an early design stage. This paper describes the approach and reports on first practical experiences.

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SMA: the smyle modeling approach

Markov chains are widely used in the context of
performance and reliability evaluation of systems of various
nature. Model checking of such chains with respect to
a given (branching) temporal logic formula has been proposed
for both the discrete [8] and the continuous time setting
[1], [3]. In this short paper, we describe the prototype
model checker $E \vdash M C^2$ for discrete and continuous-time
Markov chains, where properties are expressed in appropriate
extensions of CTL.We illustrate the general benefits
of this approach and discuss the structure of the tool.

A model checker for performance and dependability properties

Nowadays we observe a still growing interest in having greater control of domestic appliances and of interconnecting several kinds of electronic devices in the home (and similar environments) so as to obtain additional features (IEEE Special Issue 1991). Typical applications are energy management, safety, security, home automation and entertainment. Thus the conventional trend of viewing consumer products in isolation is shifting towards a more system-oriented approach—The Home System. Several home systems are currently candidate for world-wide standardization: the Home Bus System (Japan), Consumer Electronic Bus (USA), SmartHouse (USA), and Esprit Home Systems (Europe). The latter system (EHSA 1991) emerged from the cooperation of several European manufacturers and its architecture is based on the well-known OSI model (OSI 1984). The Home System has adopted four OSI layers: the Physical layer, the Data Link layer, the Network layer and the Application layer (Vlot and Katoen 1991). The Data Link layer has been subdivided into the Logical Link Control (LLC) sublayer

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A Semi-Markov Model of a Home Network Access Protocol

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.

Joost-Pieter Katoen

Papers

Performance and verification

SMA: the smyle modeling approach

A model checker for performance and dependability properties

A Semi-Markov Model of a Home Network Access Protocol

The Probabilistic Termination Tool Amber