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Automatically Inferring Quantified Loop Invariants by Algorithmic Learning from Simple Templates

机译:通过简单模板的算法学习自动推断定量环不变性

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摘要

By combining algorithmic learning, decision procedures, predicate abstraction, and simple templates, we present an automated technique for finding quantified loop invariants. Our technique can find arbitrary first-order invariants (modulo a fixed set of atomic propositions and an underlying SMT solver) in the form of the given template and exploits the flexibility in invariants by a simple randomized mechanism. The proposed technique is able to find quantified invariants for loops from the Linux source, as well as for the benchmark code used in the previous works. Our contribution is a simpler technique than the previous works yet with a reasonable derivation power.
机译:通过组合算法学习,决策过程,谓词抽象和简单模板,我们提出了一种自动技术,用于找到量化的循环不变式。我们的技术可以找到给定模板形式的任意一阶不变式(对一组固定的原子命题和一个潜在的SMT求解器进行模化),并通过简单的随机机制利用不变式的灵活性。所提出的技术能够找到来自Linux源的循环以及先前工作中使用的基准代码的量化不变式。我们的贡献是比以前的作品更简单的技术,同时具有合理的推导能力。

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