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Better Automated Importance Splitting for Transient Rare Events

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Dependable Software Engineering. Theories, Tools, and Applications (SETTA 2017)

Abstract

Statistical model checking uses simulation to overcome the state space explosion problem in formal verification. Yet its runtime explodes when faced with rare events, unless a rare event simulation method like importance splitting is used. The effectiveness of importance splitting hinges on nontrivial model-specific inputs: an importance function with matching splitting thresholds. This prevents its use by non-experts for general classes of models. In this paper, we propose new method combinations with the goal of fully automating the selection of all parameters for importance splitting. We focus on transient (reachability) properties, which particularly challenged previous techniques, and present an exhaustive practical evaluation of the new approaches on case studies from the literature. We find that using Restart simulations with a compositionally constructed importance function and thresholds determined via a new expected success method most reliably succeeds and performs very well. Our implementation within the Modest Toolset supports various classes of formal stochastic models and is publicly available.

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Notes

  1. 1.

    We rely on the standard CLT assumption for large enough sample sizes; to this end, we do not stop before we obtain at least one sample \(>0\) and at least 50 samples.

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Acknowledgements

We are grateful to José Villén-Altamirano for very helpful discussions that led to our eventual design of the expected success method.

This work is supported by the 3TU.BSR project, ERC grant 695614 (POWVER), the NWO SEQUOIA project, and SeCyT-UNC projects 05/BP12 and 05/B497.

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Correspondence to Arnd Hartmanns .

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Budde, C.E., D’Argenio, P.R., Hartmanns, A. (2017). Better Automated Importance Splitting for Transient Rare Events. In: Larsen, K., Sokolsky, O., Wang, J. (eds) Dependable Software Engineering. Theories, Tools, and Applications. SETTA 2017. Lecture Notes in Computer Science(), vol 10606. Springer, Cham. https://doi.org/10.1007/978-3-319-69483-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-69483-2_3

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