Abstract
Monte Carlo simulations may be used to efficiently estimate critical properties of complex evolving systems but are nevertheless computationally intensive. Hence, when only part of a system is new or modified it seems wasteful to re-simulate the parts that have not changed. It also seems unnecessary to perform many simulations of parts of a system whose behaviour does not vary significantly.
To increase the efficiency of designing and testing complex evolving systems we present simulation techniques to allow such a system to be verified against behaviour-preserving statistical abstractions of its environment. We propose a frequency domain metric to judge the a priori performance of an abstraction and provide an a posteriori indicator to aid construction of abstractions optimised for critical properties.
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Legay, A., Sedwards, S. (2014). Statistical Abstraction Boosts Design and Test Efficiency of Evolving Critical Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change. ISoLA 2014. Lecture Notes in Computer Science, vol 8802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45234-9_2
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DOI: https://doi.org/10.1007/978-3-662-45234-9_2
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