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Academic Journal of Computing & Information Science, 2020, 3(2); doi: 10.25236/AJCIS.2020.030212.

Runtime Probabilistic Model Checking Based on Incremental Method

Author(s)

Chao He

Corresponding Author:
Chao He
Affiliation(s)

Information Engineering, Nanjing University of Finance and Economics, Nanjing 210000, China
704218038@qq.com

Abstract

Nowadays, more and more systems change dynamically during their life cycle, runtime probabilistic model checking is proposed to verify these system. An important challenge of runtime probabilistic model checking is its performance. It should be fast enough to respond to runtime requirements and continuously verify whether the current system meets system requirements when the system changes dynamically. In this paper, in view of the efficiency of the runtime probabilistic model checking, we propose a runtime probabilistic model checking based on incremental method. The method applies the ideal of incremental verification to reuse the calculated value of the previous model to reduce the number of iterations and improve their performance. We implement our method in model checking tool PRISM, and use a benchmark case model to perform model verification on its reachability properties. The results of the experiments show that the method proposed in this paper can reduce the system verification time of standard runtime probabilistic model checking by more than 45% in most of cases.

Keywords

Runtime probabilistic model checking, Incremental verification, Stochastic system, Discrete-Time Markov Chain

Cite This Paper

Chao He. Runtime Probabilistic Model Checking Based on Incremental Method. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 2: 85-95. https://doi.org/10.25236/AJCIS.2020.030212.

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