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Automatic detection of nocuous coordination ambiguities in natural language requirements

Yang, Hui; Willis, Alistair; De Roeck, Anne and Nuseibeh, Bashar (2010). Automatic detection of nocuous coordination ambiguities in natural language requirements. In: Proceedings of the IEEE/ACM international conference on Automated software engineering, ACM, pp. 53–62.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1145/1858996.1859007
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Abstract

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 or Workshop Item
Copyright Holders: 2010 ACM
ISBN: 1-4503-0116-9, 978-1-4503-0116-9
Keywords: natural language requirements; nocuous ambiguity; coordination ambiguity; machine learning; human judgments
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23771
Depositing User: Hui Yang
Date Deposited: 27 Oct 2010 11:24
Last Modified: 16 May 2018 11:44
URI: http://oro.open.ac.uk/id/eprint/23771
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