MARCIE - Model Checking and Reachability Analysis Done EffiCIEntly

Schwarick, Martin; Heiner, Monika; Röhr, Christian

doi:10.1109/qest.2011.19

Cited by 31 publications

(40 citation statements)

References 23 publications

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“…All Petri nets were modelled with Snoopy [16] and analysed with MARCIE [13]. The experiments were carried out on a machine with 4x AMD Opteron™ 6276 with 2.3 GHz and 256GB RAM running CentOS 6.…”

Section: Case Studiesmentioning

confidence: 99%

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Simulative Model Checking of Steady State and Time-Unbounded Temporal Operators

Röhr

2013

Transactions on Petri Nets and Other Models of Concurrency VIII

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Abstract. When working with large stochastic models simulation remains the only possible analysis technique. Therefore, simulative model checking is the way to go. While finite time horizon algorithms are well known for probabilistic linear-time temporal logic, we provide an infinite time horizon procedure as well as steady state computation, based on exact stochastic simulation algorithms. We demonstrate the approach on models of the RKIP inhibited ERK pathway and angiogenetic process.

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Section: Case Studiesmentioning

confidence: 99%

“…The resulting linear system of equations can be solved numerically by iterative methods like Gauss-Seidel or Jacobi. There are several tools available that support such solvers, among them MARCIE [13]. The drawback of numerical solvers is their restriction to bounded CTMCs and the complexity is typically O (n) and in worst case O n 2 .…”

Section: Related Workmentioning

confidence: 99%

Simulative Model Checking of Steady State and Time-Unbounded Temporal Operators

Röhr

2013

Transactions on Petri Nets and Other Models of Concurrency VIII

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“…The most well known stochastic logic is CSL [5], for which various verification engines (model-checkers) exists, like Prism [3], MRMC [6] and Marcie [7], to verify behaviour of DTMC or CTMC, possibly generated from higher level languages, like the guarded command language of Prism or the stochastic Petri nets of Marcie. CSL has a predefined set of operators to specify the paths of interest (called neXt and Until), and this might constitute a limitation.…”

Section: Introductionmentioning

confidence: 99%

Improving and Assessing the Efficiency of the MC4CSLTA Model Checker

Amparore

Donatelli

2013

Computer Performance Engineering

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Abstract. CSLTA is a stochastic logic which is able to express properties on the behavior of a CTMC, in particular in terms of the possible executions of the CTMC (like the probability that the set of paths that exhibits a certain behavior is above/below a certain threshold). This paper presents the new version of the the stochastic model checker MC4CSLTA , which verifies CSL TA formulas against a Continuous Time Markov Chain, possibly expressed as a Generalized Stochastic Petri Net. With respect to the first version of the model checker presented in [1], version 2 features a totally new solution algorithm, which is able to verify complex, nested formulas based on the timed automaton, while, at the same time, is capable of reaching a time and space complexity similar to that of the CSL model checkers when the automaton specifies a neXt or an Until formulas. In particular, the goal of this paper is to present a new way of generating the MRP, which, together with the new MRP solution method presented in [2] provides the two cornerstone results which are at the basis of the current version. The model checker has been evaluated and validated against PRISM [3] (for whose CSL TA formulas which can be expressed in CSL) and against the statistical model checker Cosmos [4] (for all types of formulas).

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“…PRISM also provides statistical model checking and Gillespie simulation. The tool MARCIE [19] implements FAU but does not support cumulative rewards. Further tools that support reward properties but not FAU include, for instance, Möbius [4] and MRMC [11].…”

Section: Introductionmentioning

confidence: 99%

Computing Cumulative Rewards Using Fast Adaptive Uniformisation

Dannenberg¹,

Hahn²,

Kwiatkowska³

2013

Computational Methods in Systems Biology

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Abstract. The computation of transient probabilities for continuous-time Markov chains often employs uniformisation, also known as the Jensen's method. The fast adaptive uniformisation method introduced by Mateescu approximates the probability by neglecting insignificant states, and has proven to be effective for quantitative analysis of stochastic models arising in chemical and biological applications. However, this method has only been formulated for the analysis of properties at a given point of time t. In this paper, we extend fast adaptive uniformisation to handle expected reward properties which reason about the model behaviour until time t, for example, the expected number of chemical reactions that have occurred until t. To show the feasibility of the approach, we integrate the method into the probabilistic model checker PRISM and apply it to a range of biological models, demonstrating superior performance compared to existing techniques.

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MARCIE - Model Checking and Reachability Analysis Done EffiCIEntly

Cited by 31 publications

References 23 publications

Simulative Model Checking of Steady State and Time-Unbounded Temporal Operators

Simulative Model Checking of Steady State and Time-Unbounded Temporal Operators

Improving and Assessing the Efficiency of the MC4CSLTA Model Checker

Computing Cumulative Rewards Using Fast Adaptive Uniformisation

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