Importance Splitting for Statistical Model Checking Rare Properties

Jégourel, Cyrille; Legay, Axel; Sedwards, Sean

doi:10.1007/978-3-642-39799-8_38

Cited by 74 publications

(84 citation statements)

References 22 publications

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“…Importance splitting [11,15] is an alternative variance reduction technique with potential advantages for SMC [10]. We therefore intend to compare our current framework with an implementation of importance splitting for stochastic timed automata, applying both to substantial case studies.…”

Section: Resultsmentioning

confidence: 99%

Importance Sampling for Stochastic Timed Automata

Jégourel

Larsen

Legay

et al. 2016

Dependable Software Engineering: Theories, Tools, and Applications

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Abstract. We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state-wise change of measure is then applied on-thefly during simulations, ensuring that dead ends are never reached. The change of measure is guaranteed by construction to reduce the variance of the estimator with respect to crude Monte Carlo, while experimental results demonstrate that we can achieve substantial computational gains.

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Section: Resultsmentioning

confidence: 99%

Importance Sampling for Stochastic Timed Automata

Jégourel

Larsen

Legay

et al. 2016

Dependable Software Engineering: Theories, Tools, and Applications

Self Cite

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“…However, these contracts are 17 Future Work Our objective is to improve our solution by exploiting rare-event techniques [45] that would allow us to detect rare emergent behaviors with a minimal amount of simulations. Our second future work is to automatize the relationship between the outcome of SMC and the reconfiguration process, in order to automatically find an architecture satisfying sufficiently the GCSL contracts.…”

Section: Conclusion Future Work and Related Workmentioning

confidence: 99%

An Application of SMC to continuous validation of heterogeneous systems

Arnold¹,

Baleani²,

Ferrari³

et al. 2017

EAI Endorsed Transactions on Industrial Networks and Intelligen

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This paper considers the rigorous design of Systems of Systems (SoS), i.e. systems composed of a set of heterogeneous components whose number evolves with time. Such components cooperate to accomplish functions that they could not achieve in isolation. Examples of SoS include smart cities or airport management system. The dynamical evolution of SoS behavior and architecture makes it impossible to design an appropriate solution beforehand. Consequently, existing approaches build on an iterative process that takes SoS evolution into account. A key challenge in this process is the ability to reason about and analyze a given view of the SoS (on a fixed number of SoS constituents) with respect to a set of goals, and use the results to eventually predict the evolution of the SoS. To address this challenge, we rely on a scalable formal verification technique known as Statistical Model Checking (SMC). SMC quantifies how close the current view is from achieving a given mission. We integrate SMC with existing industrial practice, by addressing both methodological and technological issues. Our contribution is: (1) a methodology for validation of SoS formal requirements; (2) a formal specification language able to express complex SoS requirements; (3) the adoption of current industry standards for simulation and heterogeneous systems integration ; (4) a robust SMC tool-chain integrated with system design tools used in practice. We illustrate the application of our SMC tool-chain and the obtained results on a case study.

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“…Plasma Lab addresses this with the standard variance reduction techniques of importance sampling [11] and importance splitting [12,13,14].…”

Section: Rare Event Simulationmentioning

confidence: 99%

Plasma Lab: A Modular Statistical Model Checking Platform

Legay

Sedwards

Traonouez

2016

Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques

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Abstract. We present an overview of Plasma Lab, a modular statistical model checking (SMC) platform that facilitates multiple SMC algorithms, multiple modelling and query languages and has multiple modes of use. Plasma Lab may be used as a stand-alone tool with a graphical development environment or invoked from the command line for high performance scripting applications. Plasma Lab is written in Java for maximum cross-platform compatibility, but it may interface with tools and libraries written in arbitrary programming languages. Plasma Lab's API also allows it to be incorporated as a library within other tools. We first describe the motivation and architecture of Plasma Lab, then proceed to describe some of its important algorithms, including those for rare events and nondeterminism. We conclude with a number of industrially-relevant case studies and applications.

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Importance Splitting for Statistical Model Checking Rare Properties

Cited by 74 publications

References 22 publications

Importance Sampling for Stochastic Timed Automata

Importance Sampling for Stochastic Timed Automata

An Application of SMC to continuous validation of heterogeneous systems

Plasma Lab: A Modular Statistical Model Checking Platform

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