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Automatic detection of nocuous coordination ambiguities in natural language requirements
Date
2010
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.
Supervisor
Description
peer-reviewed
Publisher
Association for Computing Machinery
Citation
ASE '10, Proceedings of the IEEE/ACM International Conference on Automated Software Engineering;pp. 53-62
Files
Funding code
Funding Information
Science Foundation Ireland (SFI), EPSRC
Sustainable Development Goals
External Link
Type
Meetings and Proceedings
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/