Web文档模型检查中反例的增量构造

Franz Weitl, S. Nakajima
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引用次数: 6

摘要

针对时序描述逻辑ALCCTL,提出了一种增量生成反例的新算法。ALCCTL是描述逻辑ALC和计算树逻辑CTL的可确定组合,用于表达通过模型检查验证的web文档的内容和结构相关属性。在规范冲突的情况下,现有的模型检查器提供了一个反例,这个反例可能很大而且很复杂。我们以两种方式扩展了生成反例的现有算法。首先,最初生成一个粗略的反例,随后可以以增量的方式将其细化到所需的详细级别。其次,用户可以选择反例的精炼位置和方式。这样就可以根据用户的兴趣对错误场景进行交互式逐步分析。我们在一个基于web的培训文档的案例研究中证明,与现有模型检查器的反例相比,所提出的方法揭示了更多的错误,并更准确地解释了错误的原因。此外,我们证明了所提出的算法足够快,即使在大型文档的情况下也能实现平滑的交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incremental Construction of Counterexamples in Model Checking Web Documents
A new algorithm for incrementally generating counterexamples for the temporal description logic ALCCTL is presented. ALCCTL is a decidable combination of the description logic ALC and computation tree logic CTL that is expressive for content- and structure-related properties of web documents being verified by model checking. In the case of a specification violation, existing model checkers provide a single counterexample which may be large and complex. We extend existing algorithms for generating counterexamples in two ways. First, a coarse counterexample is generated initially that can be refined subsequently to the desired level of detail in an incremental manner. Second, the user can choose where and in which way a counterexample is refined. This enables the interactive step-by-step analysis of error scenarios according to the user’s interest. We demonstrate in a case study on a web-based training document that the proposed approach reveals more errors and explains the cause of errors more precisely than the counterexamples of existing model checkers. In addition, we demonstrate that the proposed algorithm is sufficiently fast to enable smooth interaction even in the case of large documents.
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