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k-Inductive Invariant Checking for Graph Transformation Systems

机译:图变换系统的k归纳不变检验

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While offering significant expressive power, graph transformation systems often come with rather limited capabilities for automated analysis, particularly if systems with many possible initial graphs and large or infinite state spaces are concerned. One approach that tries to overcome these limitations is inductive invariant checking. However, the verification of inductive invariants often requires extensive knowledge about the system in question and faces the approach-inherent challenges of locality and lack of context. To address that, this paper discusses k-inductive invariant checking for graph transformation systems as a generalization of inductive invariants. The additional context acquired by taking multiple (k) steps into account is the key difference to inductive invariant checking and is often enough to establish the desired invariants without requiring the iterative development of additional properties. To analyze possibly infinite systems in a finite fashion, we introduce a symbolic encoding for transformation traces using a restricted form of nested application conditions. As its central contribution, this paper then presents a formal approach and algorithm to verify graph constraints as k-inductive invariants. We prove the approach's correctness and demonstrate its applicability by means of several examples evaluated with a prototypical implementation of our algorithm.
机译:图转换系统虽然提供了强大的表达能力,但通常具有相当有限的自动化分析功能,尤其是在涉及具有许多可能的初始图以及大型或无限状态空间的系统时。尝试克服这些限制的一种方法是归纳不变检查。但是,归纳不变式的验证通常需要对所讨论系统的广泛了解,并且面临本地性和缺乏上下文的方法固有的挑战。为了解决这个问题,本文讨论了图变换系统的k归纳不变性检查,作为归纳不变性的推广。通过考虑多个(k)步骤获得的附加上下文是归纳不变检查的关键区别,并且通常足以建立所需不变,而无需迭代开发其他属性。为了以有限的方式分析可能的无限系统,我们引入了使用受限应用程序嵌套形式的转换轨迹的符号编码。作为其主要贡献,本文然后提出了一种正式的方法和算法来验证图约束为k归纳不变式。我们通过使用我们的算法的原型实现评估的几个示例,证明了该方法的正确性并证明了其适用性。

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