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dc.contributor.authorNaeem, M
dc.contributor.authorBajwa, IS
dc.contributor.authorNaeem, MA
dc.contributor.authorChaudhri, AA
dc.contributor.authorAli, S
dc.contributor.editorZhang, R
dc.contributor.editorCordeiro, J
dc.contributor.editorLi, X
dc.contributor.editorZhang, Z
dc.contributor.editorZhang, J
dc.date.accessioned2012-04-26T03:11:44Z
dc.date.available2012-04-26T03:11:44Z
dc.date.copyright2011
dc.date.issued2012-04-26
dc.identifier.citation13th International Conference on Enterprise Information Systems (ICEIS’11), China, pages 102 - 110
dc.identifier.isbn978-989-8425-54-6
dc.identifier.urihttp://hdl.handle.net/10292/4060
dc.description.abstractThe available approaches for automatically generating class models from natural language (NL) software requirements specifications (SRS) exhibit less accuracy due to informal nature of NL such as English. In the automated class model generation, a higher accuracy can be achieved by overcoming the inherent syntactic ambiguities and semantic inconsistencies in English. In this paper, we propose a SBVR based approach to generate an unambiguous representation of NL software requirements. The presented approach works as the user inputs the English specification of software requirements and the approach processes input English to extract SBVR vocabulary and generate a SBVR representation in the form of SBVR rules. Then, SBVR rules are semantically analyzed to extract OO information and finally OO information is mapped to a class model. The presented approach is also presented in a prototype tool NL2SBVRviaSBVR that is an Eclipse plugin and a proof of concept. A case study has also been solved to show that the use of SBVR in automated generation of class models from NL software requirements improves accuracy and consistency.
dc.publisherSciTePress
dc.relation.urihttp://www.iceis.org/Abstracts/2011/ICEIS_2011_Abstracts.htm
dc.rightsSciTePress Digital Library ©2011 All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version)
dc.subjectNatural Language Interface
dc.subjectControlled Natural Language
dc.subjectNatural Language Processing
dc.subjectClass Model
dc.titleA controlled Natural Language Interface to Class Models
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess


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