Lei Song, Lijun Zhang, H. Hermanns, Jens Chr. Godskesen
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引用次数: 4
摘要
概率模型检验中的抽象精化技术是验证超大或无限状态概率并发系统的重要方法。细化步骤的核心是对反例的隐式或显式分析。本文提出了一种基于增量计算may- and - must-quotient自动机序列的概率计算树逻辑(PCTL)抽象改进方法。这些都是由深度有界的增加深度的双模拟等效引起的。该方法既合理又完整,因为等效收敛于真正的PCTL等效。实验结果表明了该方法的有效性。
Abstraction refinement techniques in probabilistic model checking are prominent approaches to the verification of very large or infinite-state probabilistic concurrent systems. At the core of the refinement step lies the implicit or explicit analysis of a counterexample. This paper proposes an abstraction refinement approach for the probabilistic computation tree logic (PCTL), which is based on incrementally computing a sequence of may- and must-quotient automata. These are induced by depth-bounded bisimulation equivalences of increasing depth. The approach is both sound and complete, since the equivalences converge to the genuine PCTL equivalence. Experimental results with a prototype implementation show the effectiveness of the approach.