使用默认推理来发现自然语言需求中的不一致

D. Zowghi, V. Gervasi, Andrew McRae
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引用次数: 40

摘要

在许多研究中发现,在识别和分析来自多个涉众的需求不一致时使用逻辑是有效的。默认推理是一种理论上建立良好的形式主义,特别适合于支持需求的演变。然而,在引出需求和与涉众讨论需求时直接使用逻辑会导致严重的可用性问题。在本文中,我们探索了自然语言解析技术与默认推理的集成来克服这些困难。我们还提出了一种方法,用于自动发现暴露需求不一致的场景,并展示如何以正式的方式处理它们。这些技术是在一个叫做CARL的原型工具中实现和测试的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using default reasoning to discover inconsistencies in natural language requirements
The use of logic in identifying and analysing inconsistency in requirements from multiple stakeholders has been found to be effective in a number of studies. Default reasoning is a theoretically well founded formalism that is especially suited for supporting the evolution of requirements. However, direct use of logic in eliciting requirements and in discussing them with stakeholders poses serious useability problems. In this paper we explore the integration of natural language parsing techniques with default reasoning to overcome these difficulties. We also propose a method for automatically discovering scenarios that expose inconsistencies in requirements, and show how to deal with them in a formal manner. These techniques were implemented and tested in a prototype tool called CARL.
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