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2010
We present a compositional verification technique for systems that exhibit both probabilistic and nondeterministic behaviour. We adopt an assume-guarantee approach to verification, where both the assumptions made about system components and the guarantees that they provide are regular safety properties, represented by finite automata. Unlike previous proposals for assume-guarantee reasoning about probabilistic systems, our approach does not require that components interact in a fully synchronous fashion. In addition, the compositional verification method is efficient and fully automated, based on a reduction to the problem of multi-objective probabilistic model checking. We present asymmetric and circular assume-guarantee rules, and show how they can be adapted to form quantitative queries, yielding lower and upper bounds on the actual probabilities that a property is satisfied. Our techniques have been implemented and applied to several large case studies, including instances where conventional probabilistic verification is infeasible.
2010
We present a fully automated technique for compositional verification of probabilistic systems. Our approach builds upon a recently proposed assume-guarantee framework for probabilistic automata, in which assumptions and guarantees are probabilistic safety properties, represented using finite automata. A limitation of this work is that the assumptions need to be created manually. To overcome this, we propose a novel learning technique based on the L* algorithm, which automatically generates probabilistic assumptions using the results of queries executed by a probabilistic model checker. Learnt assumptions either establish satisfaction of the verification problem or are used to generate a probabilistic counterexample that refutes it. In the case where an assumption cannot be generated, lower and upper bounds on the probability of satisfaction are produced. We illustrate the applicability of the approach on a range of case studies.
2011
This tutorial provides an introduction to probabilistic model checking, a technique for automatically verifying quantitative properties of probabilistic systems. We focus on Markov decision processes (MDPs), which model both stochastic and nondeterministic behaviour. We describe methods to analyse a wide range of their properties, including specifications in the temporal logics PCTL and LTL, probabilistic safety properties and cost-or reward-based measures. We also discuss multiobjective probabilistic model checking, used to analyse trade-offs between several different quantitative properties. Applications of the techniques in this tutorial include performance and dependability analysis of networked systems, communication protocols and randomised distributed algorithms. Since such systems often comprise several components operating in parallel, we also cover techniques for compositional modelling and verification of multi-component probabilistic systems. Finally, we describe three large case studies which illustrate practical applications of the various methods discussed in the tutorial.
2011
We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies.
2008
Abstract Soon after the birth of the flourishing research area of model checking in the early eighties, researchers started to apply this technique to finite automata equipped with probabilities. The initial focus was on qualitative properties-eg, does a program terminate with probability one?-but later efficient algorithms were developed for quantitative questions as well. Model checking of probabilistic models received quite some attention in the late nineties, and this popularity lasts until today.
Formal Methods in System Design, 2010
A contract allows to distinguish hypotheses made on a system (the guarantees) from those made on its environment (the assumptions). In this paper, we focus on models of Assume/Guarantee contracts for (stochastic) systems. We consider contracts capable of capturing reliability and availability properties of such systems. We also show that classical notions of Satisfaction and Refinement can be checked by effective methods thanks to a reduction to classical verification problems. Finally, theorems supporting compositional reasoning and enabling the scalable analysis of complex systems are also studied.
2010
Quantitative verification techniques are able to establish system properties such as "the probability of an airbag failing to deploy on demand" or "the expected time for a network protocol to successfully send a message packet". In this paper, we describe a framework for quantitative verification of software that exhibits both real-time and probabilistic behaviour. The complexity of real software, combined with the need to capture precise timing information, necessitates the use of abstraction techniques. We outline a quantitative abstraction refinement approach, which can be used to automatically construct and analyse abstractions of probabilistic, real-time programs. As a concrete example of the potential applicability of our framework, we discuss the challenges involved in applying it to the quantitative verification of SystemC, an increasingly popular system-level modelling language.
Lecture Notes in Computer Science, 2003
The paper studies automatic verification of liveness properties with probability 1 over parameterized programs that include probabilistic transitions, and proposes two novel approaches to the problem. The first approach is based on a Planner that occasionally determines the outcome of a finite sequence of "random" choices, while the other random choices are performed non-deterministically. Using a Planner, a probabilistic protocol can be treated just like a non-probabilistic one and verified as such. The second approach is based on γ-fairness, a notion of fairness that is sound and complete for verifying simple temporal properties (whose only temporal operators are ½ and ¼ ) over finite-state systems. The paper presents a symbolic model checker based on γ-fairness. We then show how the network invariant approach can be adapted to accommodate probabilistic protocols. The utility of the Planner approach is demonstrated on a probabilistic mutual exclusion protocol. The utility of the approach of γ-fairness with network invariants is demonstrated on Lehman and Rabin's Courteous Philosophers algorithm. ⋆
2011
Abstract: Research efforts were conducted under this task order to emphasize unique technologies in support of achieving the program goals associated with the META Program. The contractor focused on technologies and technological breakthroughs addressing probabilistic verification of cyber physical system aspects. Collaboration with Honeywell International Inc., Aerospace, TTTech Computertechnik AG, and Vanderbilt University was facilitated to optimize technology development.
2012
Abstract—We address the problem of verifying PCTL properties of Markov Decision Processes whose state transition probabilities are only known to lie within uncertainty sets. Using results from convex theory and duality, we propose a suite of verication algorithms and prove their soundness, completeness and termination when arbitrary convex models of uncertainty are considered. Furthermore, soundness and termination can also be guaranteed when non-convex models of uncertainty are adopted.
2011
This paper describes a major new release of the PRISM probabilistic 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.
Applied Intelligence, 2016
In this paper, we address the problem of verifying probabilistic and epistemic properties in concurrent probabilistic systems expressed in PCTLK. PCTLK is an extension of the Probabilistic Computation Tree Logic (PCTL) augmented with Knowledge (K). In fact, PCTLK enjoys two epistemic modalities K i for knowledge and P r b K i for probabilistic knowledge. The approach presented for verifying PCTLK specifications in such concurrent systems is based on a transformation technique. More precisely, we convert PCTLK model checking into the problem of model checking Probabilistic Branching Time Logic (PBTL), which enjoys path quantifiers in the range of adversaries. We then prove that model checking a formula of PCTLK in concurrent probabilistic programs is PSPACE-complete. Furthermore, we represent models associated with PCTLK logic symbolically with Multi-Terminal Binary Decision Diagrams (MTBDDs), which are supported by the probabilistic model checker PRISM. Finally, an application, namely the NetBill online shopping payment protocol, and an example about synchronization illustrated
arXiv (Cornell University), 2008
In this paper, we present a probabilistic adaptation of an Assume/Guarantee contract formalism. For the sake of generality, we assume that the extended state machines used in the contracts and implementations define sets of runs on a given set of variables, that compose by intersection over the common variables. In order to enable probabilistic reasoning, we consider that the contracts dictate how certain input variables will behave, being either non-deterministic, or probabilistic; the introduction of probabilistic variables leading us to tune the notions of implementation, refinement and composition. As shown in the report, this probabilistic adaptation of the Assume/Guarantee contract theory preserves compositionality and therefore allows modular reliability analysis, either with a top-down or a bottom-up approach.
2009
Process algebras are a set of mathematically rigourous languages with well defined semantics that permit modelling behaviour of concurrent and communicating systems. Verification of concurrent systems within the process algebraic approach can be performed by checking that processes enjoy properties described by some temporal logic’s formulae. In this paper we present a formal framework that permits verifying properties of concurrent and communicating systems by using an assumption-guarantee approach. Each system component is not considered in isolation, but in conjunction with assumptions about the context of the component. In the paper we introduce a sound and complete proof system that permits verifying whether a process, when it is executed in an environment for which we provide some assumptions, satisfies a given formula. It is also ensured that property satisfaction is preserved whenever the context is partially instantiated (implemented) as a concrete process that verifies the assumptions we have for the environment.
Sigmetrics Performance Evaluation Review, 2005
In this paper, we describe some practical applications of probabilistic model checking, a technique for the formal analysis of systems which exhibit stochastic behaviour. We give an overview of a selection of case studies carried out using the probabilistic model checking tool PRISM, demonstrating the wide range of application domains to which these methods are applicable. We also illustrate several benefits of using formal verification techniques to analyse probabilistic systems, including: (i) that they allow a wide range of numerical properties to be computed accurately; and (ii) that they perform a complete and exhaustive analysis enabling, for example, a study of best-and worst-case scenarios.
2006
Probabilistic model checking is an automatic formal verification technique for analysing quantitative properties of systems which exhibit stochastic behaviour. PRISM is a probabilistic model checking tool which has already been successfully deployed in a wide range of application domains, from real-time communication protocols to biological signalling pathways. The tool has recently undergone a significant amount of development. Major additions include facilities to manually explore models, Monte-Carlo discrete-event simulation techniques for approximate model analysis (including support for distributed simulation) and the ability to compute cost- and reward-based measures, e.g. “the expected energy consumption of the system before the first failure occurs”. This paper presents an overview of all the main features of PRISM. More information can be found on the website: www.cs.bham.ac.uk/~dxp/prism.
Sigmetrics Performance Evaluation Review, 2009
Probabilistic model checking is a formal verification technique for the modelling and analysis of stochastic systems. It has proved to be useful for studying a wide range of quantitative properties of models taken from many different application domains. This includes, for example, performance and reliability properties of computer and communication systems. In this paper, we give an overview of the probabilistic model checking tool PRISM, focusing in particular on its support for continuous-time Markov chains and Markov reward models, and how these can be used to analyse performability properties.
2002
In this thesis, we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks. A probabilistic model checker inputs a probabilistic model and a specification, such as "the message will be delivered with probability 1", "the probability of shutdown occurring is at most 0.02" or "the probability of a leader being elected within 5 rounds is at least 0.98", and can automatically verify if the specification is true in the model.
Proceeding of the 33rd international conference on Software engineering - ICSE '11, 2011
Unpredictable changes continuously affect software systems and may have a severe impact on their quality of service, potentially jeopardizing the system's ability to meet the desired requirements. Changes may occur in critical components of the system, clients' operational profiles, requirements, or deployment environments.
Formal Methods for Industrial Critical Systems, 2012
relevant case study: the IEEE 802.3 (CSMA/CD) protocol. We also discuss two contrasting approaches to the implementation of probabilistic model checking, namely those based on numerical computation and those based on discrete-event simulation. Using results from the two tools PRISM and APMC, we summarise the advantages, disadvantages and trade-offs associated with these techniques.
Eccc, 2001
The goal of model checking is to verify the correctness of a given program, on all its inputs. The main obstacle, in many cases, is the intractably large size of the program's transition system. Property testing is a randomized method to verify whether some fixed property holds on individual inputs, by looking at a small random part of that input. We join the strengths of both approaches by introducing a new notion of probabilistic abstraction, and by extending the framework of model checking to include the use of these abstractions.
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