A data-driven CHC solver

He Zhu, Stephen Magill, S. Jagannathan
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引用次数: 58

Abstract

We present a data-driven technique to solve Constrained Horn Clauses (CHCs) that encode verification conditions of programs containing unconstrained loops and recursions. Our CHC solver neither constrains the search space from which a predicate's components are inferred (e.g., by constraining the number of variables or the values of coefficients used to specify an invariant), nor fixes the shape of the predicate itself (e.g., by bounding the number and kind of logical connectives). Instead, our approach is based on a novel machine learning-inspired tool chain that synthesizes CHC solutions in terms of arbitrary Boolean combinations of unrestricted atomic predicates. A CEGAR-based verification loop inside the solver progressively samples representative positive and negative data from recursive CHCs, which is fed to the machine learning tool chain. Our solver is implemented as an LLVM pass in the SeaHorn verification framework and has been used to successfully verify a large number of nontrivial and challenging C programs from the literature and well-known benchmark suites (e.g., SV-COMP).
数据驱动的CHC求解器
我们提出了一种数据驱动技术来解决包含无约束循环和递归的程序的验证条件编码的约束角子句(CHCs)。我们的CHC求解器既不限制推断谓词组件的搜索空间(例如,通过限制变量的数量或用于指定不变量的系数值),也不固定谓词本身的形状(例如,通过限制逻辑连接词的数量和类型)。相反,我们的方法是基于一种新颖的机器学习启发的工具链,它根据不受限制的原子谓词的任意布尔组合来合成CHC解决方案。求解器内部基于cegar的验证循环逐步从递归chc中采样具有代表性的正数据和负数据,这些数据被馈送到机器学习工具链。我们的求解器在SeaHorn验证框架中作为LLVM通道实现,并已被用于成功验证大量来自文献和知名基准套件(例如SV-COMP)的重要且具有挑战性的C程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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