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Joost-Pieter Katoen

Bio: Joost-Pieter Katoen is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Probabilistic logic & Markov chain. The author has an hindex of 63, co-authored 461 publications receiving 19043 citations. Previous affiliations of Joost-Pieter Katoen include University of Erlangen-Nuremberg & University of Twente.


Papers
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Book
25 Apr 2008
TL;DR: 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.
Abstract: 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.

4,905 citations

Book
31 May 2008
TL;DR: 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.
Abstract: 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, or 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.

1,178 citations

BookDOI
TL;DR: This chapter discusses Model-Based Testing - A Glossary, which focuses on the development of model-Based Test Case Generation and its applications in I/O-automata Based Testing.
Abstract: Testing of Finite State Machines.- I. Testing of Finite State Machines.- 1 Homing and Synchronizing Sequences.- 2 State Identification.- 3 State Verification.- 4 Conformance Testing.- II. Testing of Labeled Transition Systems.- Testing of Labeled Transition Systems.- 5 Preorder Relations.- 6 Test Generation Algorithms Based on Preorder Relations.- 7 I/O-automata Based Testing.- 8 Test Derivation from Timed Automata.- 9 Testing Theory for Probabilistic Systems.- III. Model-Based Test Case Generation.- Model-Based Test Case Generation.- 10 Methodological Issues in Model-Based Testing.- 11 Evaluating Coverage Based Testing.- 12 Technology of Test-Case Generation.- 13 Real-Time and Hybrid Systems Testing.- IV. Tools and Case Studies.- Tools and Case Studies.- 14 Tools for Test Case Generation.- 15 Case Studies.- V. Standardized Test Notation and Execution Architecture.- Standardized Test Notation and Execution Architecture.- 16 TTCN-3.- 17 UML 2.0 Testing Profile.- VI. Beyond Testing.- Beyond Testing.- 18 Run-Time Verification.- 19 Model Checking.- VII. Appendices.- Appendices.- 20 Model-Based Testing - A Glossary.- 21 Finite State Machines.- 22 Labelled Transition Systems.

443 citations

Book ChapterDOI
24 Jul 2017
TL;DR: The new probabilistic model checker Storm features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs and offers a Python API for rapid prototyping by encapsulating Storm’s fast and scalable algorithms.
Abstract: We launch the new probabilistic model checker Storm. It features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs. It supports the Prism and JANI modeling languages, probabilistic programs, dynamic fault trees and generalized stochastic Petri nets. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. It offers a Python API for rapid prototyping by encapsulating Storm’s fast and scalable algorithms. Experiments on a variety of benchmarks show its competitive performance.

370 citations

Journal ArticleDOI
TL;DR: The Markov Reward Model Checker is a software tool for verifying properties over probabilistic models that supports PCTL and CSL model checking, and their reward extensions.

319 citations


Cited by
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Book
25 Apr 2008
TL;DR: 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.
Abstract: 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.

4,905 citations

Journal ArticleDOI
TL;DR: Van Kampen as mentioned in this paper provides an extensive graduate-level introduction which is clear, cautious, interesting and readable, and could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes.
Abstract: 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.

3,647 citations

Book ChapterDOI
14 Jul 2011
TL;DR: A major new release of the PRISMprobabilistic model checker is described, adding, in particular, quantitative verification of (priced) probabilistic timed automata.
Abstract: 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.

2,377 citations

Journal ArticleDOI
TL;DR: A detailed user guide is given 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.
Abstract: 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.

2,358 citations

Book ChapterDOI
15 Feb 2011

1,876 citations