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.

DOI: https://doi.org/10.1145/1858996.1859007

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.

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