Open Access
Reliable sequential testing for statistical model checking
Daniel Reijsbergen,Pieter-Tjerk de Boer,Willem R.W. Scheinhardt,Boudewijn R. Haverkort +3 more
- 23 Sep 2013
TL;DR: A framework for comparing statistical model checking (SMC) techniques is introduced and a new, more reliable, SMC technique is proposed; it is proved its correctness, and numerically compare its performance to existing techniques.
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Abstract: We introduce a framework for comparing statistical model checking (SMC) techniques and propose a new, more reliable, SMC technique. Statistical model checking has recently been implemented in tools like UPPAAL and PRISM to be able to handle models which are too complex for numerical analysis. However, these techniques turn out to have shortcomings, most notably that the validity of their outcomes depends on parameters that must be chosen a priori. Our new technique does not have this problem; we prove its correctness, and numerically compare its performance to existing techniques.
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Citations
•Journal Article
Probabilistic verification of discrete event systems using acceptance sampling
Håkan L. S. Younes,Reid Simmons +1 more
TL;DR: In this article, a model independent procedure for verifying properties of discrete event systems is proposed, based on Monte Carlo simulation and statistical hypothesis testing, where the verification is probabilistic in two senses.
References
Sequential Tests of Statistical Hypotheses
TL;DR: A sequential test of a statistical hypothesis is defined as any statistical test procedure which gives a specific rule, at any stage of the experiment (at the n-th trial for each integral value of n), for making one of the following three decisions: (1) to accept the hypothesis being tested (null hypothesis), (2) to reject the null hypothesis, (3) to continue the experiment by making an additional observation.
PRISM: Probabilistic Symbolic Model Checker
TL;DR: PRISM has been successfully used to analyse probabilistic termination, performance, and quality of service properties for a range of systems, including randomized distributed algorithms, manufacturing systems and workstation clusters.
Statistical model checking: an overview
Axel Legay,Benoît Delahaye,Saddek Bensalem +2 more
- 01 Nov 2010
TL;DR: 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 as discussed by the authors.
Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
Håkan L. S. Younes,Reid Simmons +1 more
- 27 Jul 2002
TL;DR: A model independent procedure for verifying properties of discrete event systems based on Monte Carlo simulation and statistical hypothesis testing that is probabilistic in two senses and carried out in an anytime manner.