Identifying Nocuous Ambiguities in Natural Language Requirements
Identifying Nocuous Ambiguities in Natural Language Requirements
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DOI：
10.1109/re.2006.31
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
Francis Chantree;B. Nuseibeh;A. Roeck;A. Willis
中科院分区：
文献类型：
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作者：
Francis Chantree;B. Nuseibeh;A. Roeck;A. Willis
我们提出了一种新的技术，它可以自动提醒需求的作者存在潜在的危险的歧义。我们首先建立了有害歧义的概念，即那些可能导致误解的歧义。我们测试了我们的方法在使用和或等词时出现的并列歧义。我们的起点是来自需求语料库的模棱两可的短语的数据集，以及相关的人类对其解释的判断。然后，我们使用启发式方法，主要基于单词分布信息，自动复制这些判断。启发式方法消除了人们容易解释的歧义，将有害的歧义留给了手工分析和重写。我们报告了一系列实验，以评估我们的启发式算法相对于人类判断的性能。我们的许多启发式算法都达到了很高的精确度，当它们组合使用时，召回率会大大提高
We present a novel technique that automatically alerts authors of requirements to the presence of potentially dangerous ambiguities. We first establish the notion of nocuous ambiguities, which are those that are likely to lead to misunderstandings. We test our approach on coordination ambiguities, which occur when words such as and or are used. Our starting point is a dataset of ambiguous phrases from a requirements corpus and associated human judgements about their interpretation. We then use heuristics, based largely on word distribution information, to automatically replicate these judgements. The heuristics eliminate ambiguities which people interpret easily, leaving the nocuous ones to be analysed and rewritten by hand. We report on a series of experiments that evaluate our heuristics' performance against the human judgements. Many of our heuristics achieve high precision, and recall is greatly increased when they are used in combination