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Quantitative verification tools compute probabilities, expected rewards, or steady-state values for formal models of stochastic and timed systems. Exact results often cannot be obtained efficiently, so most tools use floating-point arithmetic in iterative algorithms that approximate the quantity of interest. Correctness is thus defined by the desired precision and determines performance. In this paper, we report on the experimental evaluation of these trade-offs performed in QComp 2020: the second friendly competition of tools for the analysis of quantitative formal models. We survey the precision guarantees—ranging from exact rational results to statistical confidence statements—offered by the nine participating tools. They gave rise to a performance evaluation using five tracks with varying correctness criteria, of which we present the results.
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Exact quantitative probabilistic model checking through rational search
Model checking systems formalized using probabilistic models such as discrete time Markov chains (DTMCs) and Markov decision processes (MDPs) can be reduced to computing constrained reachability properties. Linear programming methods to compute reachability probabilities for DTMCs and MDPs do not scale to large models. Thus, model checking tools often employ iterative methods to approximate reachability probabilities. These approximations can be far from the actual probabilities, leading to inaccurate model checking results. On the other hand, specialized techniques employed in existing state-of-the-art exact quantitative model checkers, don’t scale as well as their iterative counterparts. In this work, we present a new model checking algorithm that improves the approximate results obtained by scalable iterative techniques to compute exact reachability probabilities. Our techniques are implemented as an extension of the PRISM model checker and are evaluated against other exact quantitative model checking engines.
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- PAR ID:
- 10194353
- Date Published:
- Journal Name:
- Formal Methods in System Design
- ISSN:
- 0925-9856
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1007/s10703-020-00348-y
