Compositional Stochastic Model Checking Probabilistic Automata via Assume-guarantee Reasoning
- https://doi.org/10.2991/ijndc.k.190918.001How to use a DOI?
- Stochastic model checking, assume-guarantee reasoning, symmetric assume-guarantee rule, learning algorithm, probabilistic automata
Stochastic model checking is the extension and generalization of the classical model checking. Compared with classical model checking, stochastic model checking faces more severe state explosion problem, because it combines classical model checking algorithms and numerical methods for calculating probabilities. For dealing with this, we first apply symmetric assume-guarantee rule symmetric (SYM) for two-component systems and symmetric assume-guarantee rule for n-component systems into stochastic model checking in this paper, and propose a compositional stochastic model checking framework of probabilistic automata based on the NL* algorithm. It optimizes the existed compositional stochastic model checking process to draw a conclusion quickly, in cases the system model does not satisfy the quantitative properties. We implement the framework based on the PRISM tool, and several large cases are used to demonstrate the performance of it.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Yang Liu AU - Rui Li PY - 2020 DA - 2020/04 TI - Compositional Stochastic Model Checking Probabilistic Automata via Assume-guarantee Reasoning JO - International Journal of Networked and Distributed Computing SP - 94 EP - 107 VL - 8 IS - 2 SN - 2211-7946 UR - https://doi.org/10.2991/ijndc.k.190918.001 DO - https://doi.org/10.2991/ijndc.k.190918.001 ID - Liu2020 ER -