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Model checking linear temporal logic of rewriting formulas under localized fairness

机译:局部公平下的重写公式线性时态逻辑模型检验

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This paper presents the linear temporal logic of rewriting (LTLR) model checker under localized fairness assumptions for the Maude system. The linear temporal logic of rewriting extends linear temporal logic (LTL) with spatial action patterns that describe patterns of rewriting events. Since LTLR generalizes and extends various state-based and event-based logics, mixed properties involving both state propositions and actions, such as fairness properties, can be naturally expressed in LTLR. However, often the needed fairness assumptions cannot even be expressed as propositional temporal logic formulas because they are parametric, that is, they correspond to universally quantified temporal logic formulas. Such universal quantification is succinctly captured by the notion of localized fairness; for example, fairness is localized to the object name parameter in object fairness conditions. We summarize the foundations, and present the language design and implementation of the Maude Fair LTLR model checker, developed at the C++ level within the Maude system by extending the existing Maude LTL model checker. Our tool provides not only an efficient LTLR model checking algorithm under parameterized fairness assumptions but also suitable specification languages as part of its user interface. The expressiveness and effectiveness of the Maude Fair LTLR model checker are illustrated by five case studies. This is the first tool we are aware of that can model check temporal logic properties under parameterized fairness assumptions.
机译:本文提出了在Maude系统的局部公平性假设下的线性重写(LTLR)模型检查器的线性时序逻辑。重写的线性时序逻辑使用描述重写事件的模式的空间动作模式扩展了线性时序逻辑(LTL)。由于LTLR概括并扩展了各种基于状态和基于事件的逻辑,因此涉及状态命题和动作的混合属性(例如公平属性)自然可以在LTLR中表达。但是,所需的公平性假设通常甚至不能表示为命题时间逻辑公式,因为它们是参数性的,也就是说,它们对应于普遍量化的时间逻辑公式。这种普遍的量化被局部公平的概念简洁地捕获了。例如,在对象公平条件下,公平被本地化为对象名称参数。我们总结了基础,并通过扩展现有的Maude LTL模型检查器,介绍了在Maude系统中以C ++级别开发的Maude Fair LTLR模型检查器的语言设计和实现。我们的工具不仅提供了在参数化公平性假设下的有效LTLR模型检查算法,而且还提供了合适的规范语言作为其用户界面的一部分。五个案例研究说明了Maude Fair LTLR模型检查器的表现力和有效性。这是我们意识到的第一个可以对参数化公平性假设下的时间逻辑属性进行建模的工具。

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