Asymptotic perturbation bounds for probabilistic model checking with empirically determined probability parameters

Su, Guoxin, Feng, Yuan, Chen, Taolue and Rosenblum, David S. (2016) Asymptotic perturbation bounds for probabilistic model checking with empirically determined probability parameters. IEEE Transactions on Software Engineering, 42 (7) . pp. 623-639. ISSN 0098-5589 [Article] (doi:10.1109/TSE.2015.2508444)

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Probabilistic model checking is a verification technique that has been the focus of intensive research for over a decade. One important issue with probabilistic model checking, which is crucial for its practical significance but is overlooked by the state-of-the-art largely, is the potential discrepancy between a stochastic model and the real-world system it represents when the model is built from statistical data. In the worst case, a tiny but nontrivial change to some model quantities might lead to misleading or even invalid verification results. To address this issue, in this paper, we present a mathematical characterization of the consequences of model perturbations on the verification distance. The formal model that we adopt is a parametric variant of discrete-time Markov chains equipped with a vector norm to measure the perturbation. Our main technical contributions include a closed-form formulation of asymptotic perturbation bounds, and computational methods for two arguably most useful forms of those bounds, namely linear bounds and quadratic bounds. We focus on verification of reachability properties but also address automata-based verification of omega-regular properties. We present the results of a election of case studies that demonstrate that asymptotic perturbation bounds can accurately estimate the maximum variations of the verification results induced by the model perturbations.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science > Foundations of Computing group
Item ID: 19192
Depositing User: Taolue Chen
Date Deposited: 12 Apr 2016 09:51
Last Modified: 29 Nov 2022 21:46

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