通过可控性和协同性分析进行抽象细化

Freddy Y. C. Mang, Pei-Hsin Ho
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引用次数: 15

摘要

为了更好地对抽象模型进行形式属性验证,提出了一种新的抽象改进算法。在之前的工作中,改进要么是基于当前抽象模型的一组反例来选择的,如[5][6][7][8][9][19][20],要么是独立于任何反例来选择的,如[17]。我们(1)引入了一种新的“可控性”分析,它独立于任何特定的反例;(2)应用了一种新的“合作性”分析,从一组特定的反例中提取信息;(3)将两者结合起来,以更好地完善抽象模型。我们实现了该算法,并将其应用于验证几个现实世界的设计和属性。我们将该算法与[19]和[20]中的抽象细化算法以及[14]中基于插值的可达性分析进行了比较。实验结果表明,新算法在运行时间、抽象效率(定义见[19])和已验证属性的数量方面优于其他三种算法。
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Abstraction refinement by controllability and cooperativeness analysis
We present a new abstraction refinement algorithm to better refine the abstract model for formal property verification. In previous work, refinements are selected either based on a set of counter examples of the current abstract model, as in [5][6][7][8][9][19][20], or independent of any counter examples, as in [17]. We (1) introduce a new "controllability" analysis that is independent of any particular counter examples, (2) apply a new "cooperativeness" analysis that extracts information from a particular set of counter examples and (3) combine both to better refine the abstract model. We implemented the algorithm and applied it to verify several real-world designs and properties. We compared the algorithm against the abstraction refinement algorithms in [19] and [20] and the interpolation-based reachability analysis in [14]. The experimental results indicate that the new algorithm outperforms the other three algorithms in terms of runtime, abstraction efficiency (as defined in [19]) and the number of proven properties.
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