Abstract
Self-adaptive systems can deal with environmental changes by changing their own behaviors. Since self-adaptive systems modify their own behaviors dynamically, runtime verification is necessary to guarantee the correctness of the systems’ behaviors. Discrete time Markov chain model checking is a promising approach for implementing runtime verification; however, the computational cost of the current model checking approach increases as the number of parameterized transition probabilities increases. In this study, we conduct experiments on various instances of Markov chain models and demonstrate that repeated application of Laplace expansion leads to the large computational cost. The results suggest that an approach to reducing the number of times of performing Laplace expansion should be developed.
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This work was supported by JSPS Grants-in-Aid for Scientific Research (No. 15K00097).
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Ogawa, K., Nakagawa, H., Tsuchiya, T. (2015). An Experimental Evaluation on Runtime Verification of Self-adaptive Systems in the Presence of Uncertain Transition Probabilities. In: Bianculli, D., Calinescu, R., Rumpe, B. (eds) Software Engineering and Formal Methods. SEFM 2015. Lecture Notes in Computer Science(), vol 9509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49224-6_21
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DOI: https://doi.org/10.1007/978-3-662-49224-6_21
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