从失败的程序验证中提高反例质量

Li Huang, B. Meyer, M. Oriol
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引用次数: 1

摘要

在软件验证中,成功的自动化程序验证是最终的胜利。然而,通往这种成功的道路是由许多失败的证明尝试铺成的。当证明失败时,证明者产生的信息通常是模糊的,这使得很难知道如何进一步进行。这里报告的工作试图通过提供立即可理解的反例来帮助解决这种情况。为此,它引入了一种称为反例提取和最小化(CEAM)的方法。当证明失败时,CEAM将证明者生成的反例模型转换为清晰可理解的版本;此外,它还可以通过最小化反例样本所包含的整数值来进一步简化反例样本。我们已经实现了CEAM方法作为AutoProof验证器的扩展,并演示了它在一系列示例中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Counterexample Quality from Failed Program Verification
In software verification, a successful automated program proof is the ultimate triumph. The road to such success is, however, paved with many failed proof attempts. The message produced by the prover when a proof fails is often obscure, making it very hard to know how to proceed further. The work reported here attempts to help in such cases by providing immediately understandable counterexamples. To this end, it introduces an approach called Counterexample Extraction and Minimization (CEAM). When a proof fails, CEAM turns the counterexample model generated by the prover into a a clearly understandable version; it can in addition simplify the counterex-amples further by minimizing the integer values they contain. We have implemented the CEAM approach as an extension to the AutoProof verifier and demonstrate its application to a collection of examples.
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