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PRISM 4.0: Verification of Probabilistic Realtime Systems
"... Abstract. 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 realtime characteristics. In many application do ..."
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Cited by 225 (47 self)
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Abstract. 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 realtime 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 opensource, 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 explicitstate probabilistic model checking library; a discreteevent simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite. 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 ..."
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Cited by 36 (16 self)
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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).
Probabilistic model checking in practice: Case sudies with PRISM
"... 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 ..."
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Cited by 31 (9 self)
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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 worstcase scenarios.
Evaluating the reliability of NAND multiplexing with PRISM
 IEEE TRANSACTIONS ON COMPUTERAIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
, 2005
"... Probabilistic model checking is a formal verification technique for analysing the reliability and performance of systems exhibiting stochastic behaviour. In this paper, we demonstrate the applicability of this approach and, in particular, the probabilistic model checking tool PRISM to the evaluati ..."
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Cited by 14 (4 self)
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Probabilistic model checking is a formal verification technique for analysing the reliability and performance of systems exhibiting stochastic behaviour. In this paper, we demonstrate the applicability of this approach and, in particular, the probabilistic model checking tool PRISM to the evaluation of reliability and redundancy of defecttolerant systems in the field of computeraided design. We illustrate the technique with an example due to von Neumann, namely NAND multiplexing. We show how, having constructed a model of a defecttolerant system incorporating probabilistic assumptions about its defects, it is straightforward to compute a range of reliability measures and investigate how they are affected by slight variations in the behaviour of the system. This allows a designer to evaluate, for example, the tradeoff between redundancy and reliability in the design. We also highlight errors in analytically computed reliability bounds, recently published for the same case study.
Controller dependability analysis by probabilistic model checking
, 2006
"... We demonstrate how probabilistic model checking, a formal verification method for the analysis of systems which exhibit stochastic behaviour, can be applied to the study of dependability properties of softwarebased control systems. We provide an overview of these techniques and of the probabilist ..."
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Cited by 14 (4 self)
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We demonstrate how probabilistic model checking, a formal verification method for the analysis of systems which exhibit stochastic behaviour, can be applied to the study of dependability properties of softwarebased control systems. We provide an overview of these techniques and of the probabilistic model checking tool PRISM, illustrating the usefulness of the approach through a small case study. By using existing formalisms and tool support, we show how it is possible to construct large and complex Markov models from an intuitive highlevel description. Furthermore, we are able to take advantage of the efficient implementation techniques which have been developed for these tools.
Adaptive energy conserving algorithms for neighbor discovery in opportunistic bluetooth networks
 Selected Areas in Communications, IEEE Journal on
, 2007
"... Abstract — In this paper, we introduce and evaluate novel adaptive schemes for neighbor discovery in Bluetoothenabled adhoc networks. In an adhoc peertopeer setting, neighbor search is a continuous, hence battery draining process. In order to save energy when the device is unlikely to encounter ..."
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Cited by 12 (0 self)
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Abstract — In this paper, we introduce and evaluate novel adaptive schemes for neighbor discovery in Bluetoothenabled adhoc networks. In an adhoc peertopeer setting, neighbor search is a continuous, hence battery draining process. In order to save energy when the device is unlikely to encounter a neighbor, we adaptively choose parameter settings depending on a mobility context to decrease the expected power consumption of Bluetoothenabled devices. For this purpose, we first determine the mean discovery time and power consumption values for different Bluetooth parameter settings through a comprehensive exploration of the parameter space by means of simulation validated by experiments on real devices. The fastest average discovery time obtained is 0.2 s, while at an average discovery time of 1 s the power consumption is just 1.5 times that of the idle mode on our devices. We then introduce two adaptive algorithms for dynamically adjusting the Bluetooth parameters based on past perceived activity in the adhoc network. Both adaptive schemes for selecting the discovery mode are based only on locallyavailable information. We evaluate these algorithms in a node mobility simulation. Our adaptive algorithms reduce energy consumption by 50 % and have up to 8 % better performance over a static powerconserving scheme. Index Terms — Wireless Bluetooth networks, adhoc networks, Bluetooth neighbor discovery speed, power consumption for
Analysis of a gossip protocol in prism
 SIGMETRICS Perform. Eval. Rev
, 2008
"... Gossip protocols have been proposed as a robust and efficient method for disseminating information throughout dynamically changing networks. We present an analysis of a gossip protocol using probabilistic model checking and the tool PRISM. Since the behaviour of these protocols is both probabilist ..."
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Cited by 11 (1 self)
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Gossip protocols have been proposed as a robust and efficient method for disseminating information throughout dynamically changing networks. We present an analysis of a gossip protocol using probabilistic model checking and the tool PRISM. Since the behaviour of these protocols is both probabilistic and nondeterministic in nature, this provides a good example of the exhaustive, quantitative analysis that probabilistic model checking techniques can provide. In particular, we compute minimum and maximum values, representing the best and worstcase performance of the protocol under any scheduling, and investigate both their relationship with the average values that would be obtained through simulation and the precise scheduling which achieve these values. 1.
Analysing Robot Swarm Behaviour via Probabilistic Model Checking
"... An alternative to deploying a single robot of high complexity can be to utilize robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, faulttolerant and widely applicable. However, designing individual robot algorithms to ensure ef ..."
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Cited by 10 (0 self)
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An alternative to deploying a single robot of high complexity can be to utilize robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, faulttolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for assessing the effectiveness of any swarm algorithm before deployment are essential, such mechanisms have traditionally involved either computational simulations of swarm behaviour, or experiments with robot swarms themselves. However, such simulations or experiments cannot, by their nature, analyse all possible swarm behaviours. In this paper, we will develop and apply the use of automated probabilistic formal verification techniques to robot swarms, involving an exhaustive mathematical analysis, in order to assess whether swarms will indeed behave as required. In particular we consider a foraging robot scenario to which we apply probabilistic model checking. 1
D.: Automated verification and strategy synthesis for probabilistic systems (extended version) (2013), available from [49
"... Abstract. Probabilistic model checking is an automated technique to verify whether a probabilistic system, e.g., a distributed network protocol which can exhibit failures, satisfies a temporal logic property, for example, “the minimum probability of the network recovering from a fault in a given ti ..."
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Cited by 5 (1 self)
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Abstract. Probabilistic model checking is an automated technique to verify whether a probabilistic system, e.g., a distributed network protocol which can exhibit failures, satisfies a temporal logic property, for example, “the minimum probability of the network recovering from a fault in a given time period is above 0.98”. Dually, we can also synthesise, from a model and a property specification, a strategy for controlling the system in order to satisfy or optimise the property, but this aspect has received less attention to date. In this paper, we give an overview of methods for automated verification and strategy synthesis for probabilistic systems. Primarily, we focus on the model of Markov decision processes and use property specifications based on probabilistic LTL and expected reward objectives. We also describe how to apply multiobjective model checking to investigate tradeoffs between several properties, and extensions to stochastic multiplayer games. The paper concludes with a summary of future challenges in this area. 1
Mutation Testing from Probabilistic and Stochastic Finite State Machines
 Journal of Systems and Software
, 2009
"... Specification mutation involves mutating a specification, and for each mutation a test is derived that distinguishes the behaviours of the mutated and original specifications. This approach has been applied with finite state machines based models. This paper extends mutation testing to finite state ..."
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Cited by 4 (1 self)
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Specification mutation involves mutating a specification, and for each mutation a test is derived that distinguishes the behaviours of the mutated and original specifications. This approach has been applied with finite state machines based models. This paper extends mutation testing to finite state machine models that contain nonfunctional properties. The paper describes several ways of mutating a finite state machine with probabilities (PFSM) or stochastic time (PSFSM) attached to their transitions and shows how test sequences that distinguish between them and their mutants can be generated. Testing then involves applying each test sequence multiple times, observing the resultant output sequences and using results from statistical sampling theory in order to compare the observed frequency of each output sequence with that expected. Key words: mutation testing; probabilities; stochastic time; specification mutation 1