基于BDD子集的增量CTL模型检验

Abelardo Pardo, G. Hachtel
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引用次数: 47

摘要

提出了一种用于符号CTL模型检测的自动抽象/改进算法。因此,对完整的CTL语言进行了保守的模型检查——对通用或存在片段没有任何限制。该算法首先对初始抽象进行保守验证。如果结论是否定的,它会导出一个需要进一步解决的国家“目标集”。然后,对于这个目标集,它依次细化子公式中的近似,直到给定公式得到验证或计算资源耗尽。该方法统一适用于模型的过近似和欠近似的抽象。精化和抽象过程都基于bdd -子集。请注意,基于错误跟踪的改进程序仅限于对通用片段(或语言包含)的过度逼近,而目标集方法适用于所有一致逼近和所有CTL公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incremental CTL model checking using BDD subsetting
An automatic abstraction/refinement algorithm for symbolic CTL model checking is presented. Conservative model checking is thus done for the full CTL language-no restriction is made to the universal or existential fragments. The algorithm begins with conservative verification of an initial abstraction. If the conclusion is negative, it derives a "goal set" of states which require further resolution. It then successively refines, with respect to this goal set, the approximations made in the sub-formulas, until the given formula is verified or computational resources are exhausted. This method applies uniformly to the abstractions based in over-approximation as well as under-approximations of the model. Both the refinement and the abstraction procedures are based in BDD-subsetting. Note that refinement procedures which are based on error traces, are limited to over-approximation on the universal fragment (or for language containment), whereas the goal set method is applicable to all consistent approximations, and for all CTL formulas.
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