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Constraint Reusing and k-Induction for Three-Valued Bounded Model Checking

机译:三值有界模型检查的约束重用和k归纳

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Refinement-based model checking is an approach to software verification: Starting with an abstract software model, the model is iter-atively refined until it is precise enough to prove or refute the property of interest. A downside is that it typically takes several iterations until the necessary precision is reached, and thus, resources are spent on repeating work that has already been performed in previous iterations. We tackle this by introducing a concept for reusing information between refinement iterations in order to reduce the computational overhead. Our approach extends our previous work on three-valued abstraction (3VA) and bounded model checking (BMC). 3VA allows to translate a verification problem into a SAT-encoded three-valued BMC problem that can be checked via a SAT solver. While there was formerly no information sharing between refinement iterations, we now show that logic constraints learned by the solver in the current iteration are also valid in future iterations. Reusing such constraints enables to prune the search space of SAT which leads to a speed-up of the iterative approach. Since we previously used standard BMC, the technique was incomplete and could be only used for detecting property violations but not for proving their absence. Here we combine three-valued BMC with k-induction, which makes the approach complete for model checking safety properties.
机译:基于优化的模型检查是一种软件验证方法:从抽象的软件模型开始,对模型进行迭代改进,直到其足够精确到足以证明或驳斥感兴趣的属性为止。不利之处在于,通常需要进行多次迭代才能达到必要的精度,因此,资源会花费在重复先前迭代中已经执行的工作上。为了解决这个问题,我们引入了一种在细化迭代之间重用信息的概念,以减少计算开销。我们的方法扩展了我们先前在三值抽象(3VA)和有界模型检查(BMC)方面的工作。 3VA可以将验证问题转换为SAT编码的三值BMC问题,可以通过SAT解算器对其进行检查。虽然以前在细化迭代之间没有信息共享,但是现在我们证明,求解器在当前迭代中学习到的逻辑约束在以后的迭代中也有效。重用这样的约束使得可以修剪SAT的搜索空间,从而加快了迭代方法的速度。由于我们以前使用的是标准BMC,因此该技术是不完整的,只能用于检测违反属性的行为,而不能用于证明其不存在。在这里,我们将三值BMC与k归纳法相结合,这使得该方法对于模型检查安全性而言是完整的。

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