Yang, Hui; Willis, Alistair; De Roeck, Anne and Nuseibeh, Bashar
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|DOI (Digital Object Identifier) Link:||http://dx.doi.org/10.1145/1858996.1859007|
|Google Scholar:||Look up in Google Scholar|
Natural language is prevalent in requirements documents. However, ambiguity is an intrinsic phenomenon of natural language, and is therefore present in all such documents. Ambiguity occurs when a sentence can be interpreted differently by different readers. In this paper, we describe an automated approach for characterizing and detecting so-called nocuous ambiguities, which carry a high risk of misunderstanding among different readers. Given a natural language requirements document, sentences that contain specific types of ambiguity are first extracted automatically from the text. A machine learning algorithm is then used to determine whether an ambiguous sentence is nocuous or innocuous, based on a set of heuristics that draw on human judgments, which we collected as training data. We implemented a prototype tool for Nocuous Ambiguity Identification (NAI), in order to illustrate and evaluate our approach. The tool focuses on coordination ambiguity. We report on the results of a set of experiments to assess the performance and usefulness of the approach.
|Item Type:||Conference Item|
|Copyright Holders:||2010 ACM|
|Keywords:||natural language requirements; nocuous ambiguity; coordination ambiguity; machine learning; human judgments|
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Hui Yang|
|Date Deposited:||27 Oct 2010 11:24|
|Last Modified:||22 Mar 2015 17:02|
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