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Probabilistic ModelChecking Support for FMEA
"... Failure Mode and Effect Analysis (FMEA) is a method for assessing causeconsequence relations between component faults and hazards that may occur during the lifetime of a system. The analysis is typically time intensive and informal, and for this reason FMEA has been extended with traditional model ..."
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Cited by 16 (3 self)
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Failure Mode and Effect Analysis (FMEA) is a method for assessing causeconsequence relations between component faults and hazards that may occur during the lifetime of a system. The analysis is typically time intensive and informal, and for this reason FMEA has been extended with traditional model checking support. Such support does not take into account the probabilities associated with a component fault occurring, yet such information is crucial to developing hazard reduction strategies for a system. In this paper we propose a method for FMEA which makes use of probabilistic fault injection and probabilistic model checking. Based on this approach safety engineers are able to formally identify if a failure mode occurs with a probability higher than its tolerable hazard rate.
Approximate verification of the symbolic dynamics of Markov chains. Technical report available at http://www.crans.org/˜genest/AAGT12.pdf
"... Abstract—A finite state Markov chain M is often viewed as a probabilistic transition system. An alternative view which we follow here is to regard M as a linear transform operating on the space of probability distributions over its set of nodes. The novel idea here is to discretize the probability ..."
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Cited by 9 (0 self)
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Abstract—A finite state Markov chain M is often viewed as a probabilistic transition system. An alternative view which we follow here is to regard M as a linear transform operating on the space of probability distributions over its set of nodes. The novel idea here is to discretize the probability value space [0,1] into a finite set of intervals. A concrete probability distribution over the nodes is then symbolically represented as a tuple D of such intervals. The ith component of the discretized distribution D will be the interval in which the probability of node i falls. The set of discretized distributions is a finite set and each trajectory, generated by repeated applications of M to an initial distribution, will induce a unique infinite string over this finite set of letters. Hence, given a set of initial distributions, the symbolic dynamics of M will consist of an infinite language L over the finite alphabet of discretized distributions. We investigate whether L
INFAMY: An infinitestate Markov model checker
 In CAV
, 2009
"... Abstract. The design of complex concurrent systems often involves intricate performance and dependability considerations. Continuoustime Markov chains (CTMCs) are a widely used modeling formalism, where performance and dependability properties are analyzable by model checking. We present INFAMY, a ..."
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Cited by 9 (1 self)
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Abstract. The design of complex concurrent systems often involves intricate performance and dependability considerations. Continuoustime Markov chains (CTMCs) are a widely used modeling formalism, where performance and dependability properties are analyzable by model checking. We present INFAMY, a model checker for arbitrarily structured infinitestate CTMCs. It checks probabilistic timing properties expressible in continuous stochastic logic (CSL). Conventional model checkers explore the given model exhaustively, which is often costly, due to state explosion, and impossible if the model is infinite. INFAMY only explores the model up to a finite depth, with the depth bound being computed onthefly. The computation of depth bounds is configurable to adapt to the characteristics of different classes of models. 1 Introducing INFAMY Continuoustime Markov chains (CTMCs) are widely used in performance and dependability analysis and biological modeling. Properties are typically specified in continuous stochastic logic (CSL) [1], a logic inspired by CTL. In CSL, the until operator is equipped with a time interval to express properties such as: “The probability to reach a goal within 2 hours while maintaining a probability of at least 0.5 of communicating ( ( periodically (every five minutes) with a base station, is at least 0.9 ” via P≥0.9 P≥0.5✸≤5communicate) U ≤120 goal). CSL model checking amounts to analysis of the transient (timedependent) probability vectors [1], typically carried out by uniformization, where the transient probability is expressed by a weighted infinite sum (weights are given by a Poisson process). The standard methodology in CSL model checking is to truncate the infinite sum up to some prespecified accuracy [2]. Outside the model checking arena, ideas have been developed [3,4,5] which not only truncate the infinite sum, but also the matrix representing the system, which admits transient analysis of CTMCs with large or even infinite state spaces, provided they are given implicitly in a This work is supported by the NWODFG bilateral project VOSS, by the DFG as
Eager Markov chains
 In Proc. ATVA ’06, 4Ø�Int. Symp. on Automated Technology for Verification and Analysis
, 2006
"... Abstract. We consider infinitestate discrete Markov chains which are eager: the probability of avoiding a defined set of final states for more thanÒsteps is bounded by some exponentially decreasing function�(Ò). We prove that eager Markov chains include those induced by Probabilistic Lossy Channel ..."
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Abstract. We consider infinitestate discrete Markov chains which are eager: the probability of avoiding a defined set of final states for more thanÒsteps is bounded by some exponentially decreasing function�(Ò). We prove that eager Markov chains include those induced by Probabilistic Lossy Channel Systems, Probabilistic Vector Addition Systems with States, and Noisy Turing Machines, and that the bounding function�(Ò) can be effectively constructed for them. Furthermore, we study the problem of computing the expected reward (or cost) of runs until reaching the final states, where rewards are assigned to individual runs by computable reward functions. For eager Markov chains, an effective path exploration scheme, based on forward reachability analysis, can be used to approximate the expected reward upto an arbitrarily small error. 1
PROBABILISTIC MODELING AND VERIFICATION OF LARGE SCALE SYSTEMS BY
"... ii Large scale networked embedded systems are becoming increaingly popular with the technology advances in wireless network, energy efficient hardware, and cost effective manufacturing. Parameter tuning of such large scale systems has a significant effect on the performance metrics such as reliabili ..."
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ii Large scale networked embedded systems are becoming increaingly popular with the technology advances in wireless network, energy efficient hardware, and cost effective manufacturing. Parameter tuning of such large scale systems has a significant effect on the performance metrics such as reliability, availability, longevity, and energy consumption. The parameter tuning should be based on a performance evaluation which requires a model that closely represents the real system. In modeling a large scale system, an abstraction of the state space of the system is necessary in order to avoid the state explosion problem. Moreover, users not only, need an expressive and accurate way to describe the performance criteria in terms of the model, they also need a way to evaluate the performance criteria against the model. They can do such evaluation manually for certain wellknown properties or they can explore unknown properties with the aid of computers. Many large scale systems have a stochastic behavior. Such stochastic behavior is the result of the randomness in the systems ’ operating environments. It can also be the result of the use of randomized protocols that are used to reduce the need for costly synchronization. We abstract a system state as a probability mass
Probabilistic Transitions for P Systems ∗
"... In this paper we use the abstract syntax and the structural operational semantics of the P systems given in [1], and add probabilities to the rules and to the communication targets. We take into account the number of possible combinations of rules which can be applied in a computation step, as well ..."
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In this paper we use the abstract syntax and the structural operational semantics of the P systems given in [1], and add probabilities to the rules and to the communication targets. We take into account the number of possible combinations of rules which can be applied in a computation step, as well as the consumption degree of the current resources. 1