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• W2327602015 abstract "Model-based reliability estimation of systems can provide useful insights early in the development process. However, computational complexity of estimating metrics such as mean time to first failure (MTTFF), turnaround time (TAT), or other domain-based quantitative measures can be prohibitive both in time, space, and precision. In this article, we present an alternative to exhaustive model exploration, as in probabilistic model checking, and partial random exploration, as in statistical model checking. Our hypothesis is that a (carefully crafted) partial systematic exploration of a system model can provide better bounds for these quantitative model metrics at lower computation cost. We present a novel automated technique for metric estimation that combines simulation, invariant inference, and probabilistic model checking. Simulation produces a probabilistically relevant set of traces from which a state invariant is inferred. The invariant characterises a partial model, which is then exhaustively explored using probabilistic model checking. We report on experiments that suggest that metric estimation using this technique (for both fully probabilistic models and those exhibiting nondeterminism) can be more effective than (full-model) probabilistic and statistical model checking, especially for system models for which the events of interest are rare." @default.
• W2327602015 created "2016-06-24" @default.
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• W2327602015 date "2016-04-06" @default.
• W2327602015 modified "2023-10-07" @default.
• W2327602015 title "Less is More" @default.
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• W2327602015 doi "https://doi.org/10.1145/2890494" @default.
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