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Concordia University
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Can Text Mining Assistants Help to Improve Requirements Specifications?

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TitleCan Text Mining Assistants Help to Improve Requirements Specifications?
Publication TypeConference Paper
Year of Publication2012
AuthorsSateli, B., E. Angius, S. S. Rajivelu, and R. Witte
Refereed DesignationRefereed
Conference NameMining Unstructured Data (MUD 2012)
Date PublishedOctober 17
Conference LocationKingston, Ontario, Canada
Type of WorkPaper
Keywordsnatural language processing, Requirements Engineering, semantic Wiki
Abstract

Software requirements specifications are commonly written in natural language, making them prone to a number of defects, such as ambiguity, inconsistency, or lack of readability. Natural Language Processing (NLP) techniques have been proposed as a means to (semi-)automatically improve requirements specifications, but so far have not been widely adopted. We integrated a number of text mining assistants into a wiki-based requirements engineering platform to investigate two key questions: Can software engineers without prior training in NLP effectively leverage these techniques? And are text mining assistants actually helpful in improving the quality of a specification? Results obtained during two software engineering courses demonstrate that both are indeed the case.

URLhttp://sailhome.cs.queensu.ca/mud/res/sateli-mud2012.pdf
Copyright

© 2012 Bahar Sateli, Elian Angius, Srinivasan Sembakkam Rajivelu and René Witte

AttachmentSize
mud2012-ReqWiki.pdf375.44 KB