Proof Scores: A Survey

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Adrián Riesco, Kazuhiro Ogata, Masaki Nakamura, Daniel Gaina, Duong Dinh Tran, Kokichi Futatsugi
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引用次数: 0

Abstract

Proof scores can be regarded as outlines of the formal verification of system properties. They have been historically used by the OBJ family of specification languages. The main advantage of proof scores is that they follow the same syntax as the specification language they are used in, so specifiers can easily adopt them and use as many features as the particular language provides. In this way, proof scores have been successfully used to prove properties of a large number of systems and protocols. However, proof scores also present a number of disadvantages that prevented a large audience from adopting them as proving mechanism. In this paper we present the theoretical foundations of proof scores; the different systems where they have been adopted and their latest developments; the classes of systems successfully verified using proof scores, including the main techniques used for it; the main reasons why they have not been widely adopted; and finally we discuss some directions of future work that might solve the problems discussed previously.
证明分数:一项调查
证明分数可以看作是系统属性的形式验证的轮廓。它们在历史上一直被OBJ规范语言家族所使用。证明分数的主要优点是,它们遵循与使用它们的规范语言相同的语法,因此说明者可以很容易地采用它们,并使用特定语言提供的尽可能多的特性。通过这种方式,证明分数已经成功地用于证明大量系统和协议的性质。然而,证明分数也存在一些缺点,阻碍了大量受众采用它们作为证明机制。本文给出了证明分数的理论基础;已采用的不同制度及其最新发展;使用证明分数成功验证的系统类别,包括其使用的主要技术;它们没有被广泛采用的主要原因;最后,我们讨论了一些可能解决前面讨论的问题的未来工作方向。
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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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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