Results 1  10
of
38
Probabilistic Verification of Discrete Event Systems using Acceptance Sampling
 In Proc. 14th International Conference on Computer Aided Verification, volume 2404 of LNCS
, 2002
"... We propose a model independent procedure for verifying properties of discrete event systems. The dynamics of such systems can be very complex, making them hard to analyze, so we resort to methods based on Monte Carlo simulation and statistical hypothesis testing. The verification is probabilistic in ..."
Abstract

Cited by 86 (10 self)
 Add to MetaCart
(Show Context)
We propose a model independent procedure for verifying properties of discrete event systems. The dynamics of such systems can be very complex, making them hard to analyze, so we resort to methods based on Monte Carlo simulation and statistical hypothesis testing. The verification is probabilistic in two senses. First, the properties, expressed as CSL formulas, can be probabilistic. Second, the result of the verification is probabilistic, and the probability of error is bounded by two parameters passed to the verification procedure. The verification of properties can be carried out in an anytime manner by starting off with loose error bounds, and gradually tightening these bounds.
Model Checking for Probability and Time: From Theory to Practice
 In Proc. Logic in Computer Science
, 2003
"... Probability features increasingly often in software and hardware systems: it is used in distributed coordination and routing problems, to model faulttolerance and performance, and to provide adaptive resource management strategies. Probabilistic model checking is an automatic procedure for establi ..."
Abstract

Cited by 51 (1 self)
 Add to MetaCart
(Show Context)
Probability features increasingly often in software and hardware systems: it is used in distributed coordination and routing problems, to model faulttolerance and performance, and to provide adaptive resource management strategies. Probabilistic model checking is an automatic procedure for establishing if a desired property holds in a probabilistic model, aimed at verifying probabilistic specifications such as "leader election is eventually resolved with probability 1", "the chance of shutdown occurring is at most 0.01%", and "the probability that a message will be delivered within 30ms is at least 0.75". A probabilistic model checker calculates the probability of a given temporal logic property being satisfied, as opposed to validity. In contrast to conventional model checkers, which rely on reachability analysis of the underlying transition system graph, probabilistic model checking additionally involves numerical solutions of linear equations and linear programming problems. This paper reports our experience with implementing PRISM (www.cs.bham.ac.uk/dxp/ prism/), a Probabilistic Symbolic Model Checker, demonstrates its usefulness in analysing realworld probabilistic protocols, and outlines future challenges for this research direction.
Statistical Model Checking of BlackBox Probabilistic Systems
 In 16th conference on Computer Aided Verification (CAV’04), volume 3114 of LNCS
, 2004
"... We propose a new statistical approach to analyzing stochastic systems against specifications given in a sublogic of continuous stochastic logic (CSL). Unlike past numerical and statistical analysis methods, we assume that the system under investigation is an unknown, deployed blackbox that can be p ..."
Abstract

Cited by 47 (7 self)
 Add to MetaCart
(Show Context)
We propose a new statistical approach to analyzing stochastic systems against specifications given in a sublogic of continuous stochastic logic (CSL). Unlike past numerical and statistical analysis methods, we assume that the system under investigation is an unknown, deployed blackbox that can be passively observed to obtain sample traces, but cannot be controlled. Given a set of executions (obtained by Monte Carlo simulation) and a property, our algorithm checks, based on statistical hypothesis testing, whether the sample provides evidence to conclude the satisfaction or violation of a property, and computes a quantitative measure (pvalue of the tests) of confidence in its answer; if the sample does not provide statistical evidence to conclude the satisfaction or violation of the property, the algorithm may respond with a "don't know" answer. We implemented our algorithm in a Javabased prototype tool called VeStA, and experimented with the tool using case studies analyzed in [15]. Our empirical results show that our approach may, at least in some cases, be faster than previous analysis methods.
On statistical model checking of stochastic systems
 In Etessami, K., Rajamani, S.K., eds.: CAV. Volume 3576 of Lecture Notes in Computer Science
, 2005
"... Abstract. Statistical methods to model check stochastic systems have been, thus far, developed only for a sublogic of continuous stochastic logic (CSL) that does not have steady state operator and unbounded until formulas. In this paper, we present a statistical model checking algorithm that also ve ..."
Abstract

Cited by 35 (2 self)
 Add to MetaCart
(Show Context)
Abstract. Statistical methods to model check stochastic systems have been, thus far, developed only for a sublogic of continuous stochastic logic (CSL) that does not have steady state operator and unbounded until formulas. In this paper, we present a statistical model checking algorithm that also verifies CSL formulas with unbounded untils. The algorithm is based on Monte Carlo simulation of the model and hypothesis testing of the samples, as opposed to sequential hypothesis testing. We have implemented the algorithm in a tool called VESTA, and found it to be effective in verifying several examples. 1
Beyond Memoryless Distributions: Model Checking SemiMarkov Chains
 In Proceedings of the Joint International Workshop on Process Algebra and Probabilistic Methods, Performance Modeling and Verification, volume 2165 of LNCS
, 2001
"... Recent investigationsh vesh wnthW th automated verification of continuoustime Markov chWbL (CTMCs) against CSL (Continuous Stoch#bWb Logic) can be performed in arathW e#cient manner. Th statehatex# time distributions in CTMCs are restricted to negative exponential distributions.Ths paper investigat ..."
Abstract

Cited by 25 (4 self)
 Add to MetaCart
Recent investigationsh vesh wnthW th automated verification of continuoustime Markov chWbL (CTMCs) against CSL (Continuous Stoch#bWb Logic) can be performed in arathW e#cient manner. Th statehatex# time distributions in CTMCs are restricted to negative exponential distributions.Ths paper investigates model ch8 king of semiMarkov ch### (SMCs), a model inwhW h statehatexW times are governed by general distributions. We report on th semantical issues of adopting CSL for specifying properties of SMCs and present model chb kingalgorithx for thx logic. 1
Quantitative Verification: Models, Techniques and Tools
, 2007
"... Automated verification is a technique for establishing if certain properties, usually expressed in temporal logic, hold for a system model. The model can be defined using a highlevel formalism or extracted directly from software using methods such as abstract interpretation. The verification procee ..."
Abstract

Cited by 24 (13 self)
 Add to MetaCart
(Show Context)
Automated verification is a technique for establishing if certain properties, usually expressed in temporal logic, hold for a system model. The model can be defined using a highlevel formalism or extracted directly from software using methods such as abstract interpretation. The verification proceeds through exhaustive exploration of the statetransition graph of the model and is therefore more powerful than testing. Quantitative verification is an analogous technique for establishing quantitative properties of a system model, such as the probability of battery power dropping below minimum, the expected time for message delivery and the expected number of messages lost before protocol termination. Models analysed through this method are typically variants of Markov chains, annotated with costs and rewards that describe resources and their usage during execution. Properties are expressed in temporal logic extended with probabilistic and reward operators. Quantitative verification involves a combination of a traversal of the statetransition graph of the model and numerical computation. This paper gives a brief overview of current research in quantitative verification, concentrating on the potential of the method and outlining future challenges. The modelling approach is described and the usefulness of the methodology illustrated with an example of a realworld protocol standard – Bluetooth device discovery – that has been analysed using the PRISM model checker (www.prismmodelchecker.org).
MoDeST  A Modelling and Description Language for Stochastic Timed Systems
, 2001
"... This paper presents a modelling language, called MoDeST, for describingth beh viour of discrete event systems.Th language combines conventional programming constructs  such as iteration, alternatives, atomic statements, and exception hceptio with means to describe complexsystems in a ..."
Abstract

Cited by 23 (8 self)
 Add to MetaCart
This paper presents a modelling language, called MoDeST, for describingth beh viour of discrete event systems.Th language combines conventional programming constructs  such as iteration, alternatives, atomic statements, and exception hceptio with means to describe complexsystems in a compositional manner. In addition, MoDeST incorporates means to describe important ph[flL8xI such as nondeterminism, probabilistic branchanc and hdx realtime as well as soft realtime (i.e., stoch8Lfl'fl aspects.Th language is influenced by popular and userfriendly specification languages such as Promela, and dealswith compositionality in aligh tweigh t processalgebra style.Th us, MoDeST (i) covers a very broad spectrum of modelling concepts, (ii) possesses a rigid, processalgebra style semantics, and (iii) yet provides modern and flexible specification constructs.
Abstract interpretation of programs as Markov decision processes
 Science of Computer Programming 58
, 2005
"... Abstract. We propose a formal language for the specification of trace properties of probabilistic, nondeterministic transition systems, encompassing the properties expressible in Linear Time Logic. Those formulas are in general undecidable on infinite deterministic transition systems and thus on inf ..."
Abstract

Cited by 22 (0 self)
 Add to MetaCart
(Show Context)
Abstract. We propose a formal language for the specification of trace properties of probabilistic, nondeterministic transition systems, encompassing the properties expressible in Linear Time Logic. Those formulas are in general undecidable on infinite deterministic transition systems and thus on infinite Markov decision processes. This language has both a semantics in terms of sets of traces, as well as another semantics in terms of measurable functions; we give and prove theorems linking the two semantics. We then apply abstract interpretationbased techniques to give upper bounds on the worstcase probability of the studied property. We propose an enhancement of this technique when the state space is partitioned — for instance along the program points —, allowing the use of faster iteration methods. 1
Verification and Planning for Stochastic Processes with Asynchronous Events
, 2005
"... � Asynchronous processes are abundant in the real world � Telephone system, computer network, etc. ..."
Abstract

Cited by 20 (3 self)
 Add to MetaCart
(Show Context)
� Asynchronous processes are abundant in the real world � Telephone system, computer network, etc.
Stochastic hybrid models: An overview
 In Proceedings IFAC Conference on Analysis and Design of Hybrid Systems
, 2003
"... Abstract: An overview of Stochastic Hybrid Models developed in the literature is presented. Attention is concentrated on three classes of models: Piecewise Deterministic Markov Processes, Switching Diffusion Processes and Stochastic Hybrid Systems. The descriptive power of the three classes is compa ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
(Show Context)
Abstract: An overview of Stochastic Hybrid Models developed in the literature is presented. Attention is concentrated on three classes of models: Piecewise Deterministic Markov Processes, Switching Diffusion Processes and Stochastic Hybrid Systems. The descriptive power of the three classes is compared and conditions under which the classes coincide are developed. The theoretical analysis is motivated by modelling problems in Air Traffic Management. Copyright, 2003, IFAC