Yang, H. orcid.org/0000-0002-3372-4801, Willis, A., de Roeck, A. et al. (1 more author) (2010) Automatic Detection of Nocuous Ambiguities in Natural Language Requirements. In: Proceedings of the IEEE/ACM international conference on automated software engineering. IEEE/ACM international conference on automated software engineering, September 20–24, 2010, Antwerp, Belgium. ACM , New York , pp. 53-62.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2010 ACM |
Keywords: | Natural language requirements; nocuous ambiguity; coordination ambiguity; machine learning; human judgments |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Jun 2017 10:58 |
Last Modified: | 13 Jun 2017 10:58 |
Published Version: | http://dl.acm.org/citation.cfm?id=1859007 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110487 |