软件模型检查中的Craig vs. Newton

Daniel Dietsch, Matthias Heizmann, B. Musa, Alexander Nutz, A. Podelski
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引用次数: 10

摘要

自从SLAM和BLAST的开创性工作以来,用反例引导的抽象细化(CEGAR)进行软件模型检查一直是一个活跃的研究课题。这里的关键过程是分析一系列程序语句(反例),为程序的整体证明找到构建块。我们可以区分两种方法(我们将其命名为Craig和Newton)来实现该过程。历史上第一种方法是Newton(以SLAM工具包中的工具命名),它基于符号执行。第二种方法Craig是基于Craig插值。人们普遍认为克雷格比牛顿更有效率。事实上,SV-COMP中15个基于cegar的工具中有12个是基于Craig的。然而,基于Craig的软件模型检查器的进步只能与使用Craig插值的SMT求解器的进步同步。也许是时候重新审视牛顿,问问牛顿是否能像克雷格一样有效。我们在两种不同的最先进的软件模型检查工具中实现了Craig和Newton的总共11个变体,并给出了我们实验比较的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Craig vs. Newton in software model checking
Ever since the seminal work on SLAM and BLAST, software model checking with counterexample-guided abstraction refinement (CEGAR) has been an active topic of research. The crucial procedure here is to analyze a sequence of program statements (the counterexample) to find building blocks for the overall proof of the program. We can distinguish two approaches (which we name Craig and Newton) to implement the procedure. The historically first approach, Newton (named after the tool from the SLAM toolkit), is based on symbolic execution. The second approach, Craig, is based on Craig interpolation. It was widely believed that Craig is substantially more effective than Newton. In fact, 12 out of the 15 CEGAR-based tools in SV-COMP are based on Craig. Advances in software model checkers based on Craig, however, can go only lockstep with advances in SMT solvers with Craig interpolation. It may be time to revisit Newton and ask whether Newton can be as effective as Craig. We have implemented a total of 11 variants of Craig and Newton in two different state-of-the-art software model checking tools and present the outcome of our experimental comparison.
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