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Property-Directed Inference of Universal Invariants or Proving Their Absence

机译:不变量的属性定向推断或证明其不存在

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摘要

We present Universal Property Directed Reachability (PDR~∀), a property-directed semi-algorithm for automatic inference of invariants in a universal fragment of first-order logic. PDR~∀ is an extension of Bradley's PDR/IC3 algorithm for inference of propositional invariants. PDR~∀ terminates when it discovers a concrete counterexample, infers an inductive universal invariant strong enough to establish the desired safety property, or finds a proof that such an invariant does not exist. PDR~∀ is not guaranteed to terminate. However, we prove that under certain conditions, for example, when reasoning about programs manipulating singly linked lists, it does. We implemented an analyzer based on PDR~∀ and applied it to a collection of list-manipulating programs. Our analyzer was able to automatically infer universal invariants strong enough to establish memory safety and certain functional correctness properties, show the absence of such invariants for certain natural programs and specifications, and detect bugs. All this without the need for user-supplied abstraction predicates.
机译:我们提出了通用属性定向可达性(PDR〜∀),这是一种属性定向半算法,用于自动推导一阶逻辑通用片段中的不变量。 PDR〜∀是Bradley PDR / IC3算法的扩展,用于推断命题不变量。当发现一个具体的反例时,PDR终止,推断出一个足以建立所需安全特性的归纳通用不变式,或者找到不存在这种不变式的证据。 PDR〜∀不能保证终止。但是,我们证明了在某些条件下,例如,当推理程序处理单链表时,它确实可以。我们实现了一个基于PDR〜∀的分析器,并将其应用于一系列列表处理程序。我们的分析仪能够自动推断足够强大的通用不变量,以建立内存安全性和某些功能正确性属性,显示某些自然程序和规范中不存在此类不变量,并检测错误。所有这些都不需要用户提供抽象谓词。

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