Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects

Asadabadi, MR; Saberi, M; Zwikael, O; Chang, E

Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects

Asadabadi, MR Saberi, M

Zwikael, O Chang, E

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Computers and Industrial Engineering, 2020, 149, pp. 106828-106828
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Asadabadi, MR
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Zwikael, O
dc.contributor.author	Chang, E
dc.date.accessioned	2020-10-29T03:43:50Z
dc.date.available	2020-10-29T03:43:50Z
dc.date.issued	2020-11-01
dc.identifier.citation	Computers and Industrial Engineering, 2020, 149, pp. 106828-106828
dc.identifier.issn	0360-8352
dc.identifier.uri	http://hdl.handle.net/10453/143567
dc.description.abstract	© 2020 Elsevier Ltd In large-scale projects, the client defines a set of product requirements, which the provider is then expected to deliver within the agreed time, cost and scope. If a client sets ambiguous requirements for the project, this may result in the receipt of an unsatisfactory product. Therefore, reducing ambiguity in product requirements by the client is a critical success factor. Despite its significance and regular occurrence, the requirement ambiguity problem has not yet received a methodological solution that can fit large-scale projects, which commonly include a great number of requirements. This paper proposes a semi-automated approach, which combines natural language processing (NLP) to identify ambiguous terms and statements and a soft computing technique to specify these terms using fuzzy set theory. This work contributes to the current literature on requirement specification by highlighting a line of research which paves the way to leverage the applications of advanced tools to allow the clarification of ambiguous requirements in large-scale projects.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Computers and Industrial Engineering
dc.relation.isbasedon	10.1016/j.cie.2020.106828
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects
dc.type	Journal Article
utslib.citation.volume	149
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-29T03:43:44Z
pubs.publication-status	Accepted
pubs.volume	149

Abstract:

© 2020 Elsevier Ltd In large-scale projects, the client defines a set of product requirements, which the provider is then expected to deliver within the agreed time, cost and scope. If a client sets ambiguous requirements for the project, this may result in the receipt of an unsatisfactory product. Therefore, reducing ambiguity in product requirements by the client is a critical success factor. Despite its significance and regular occurrence, the requirement ambiguity problem has not yet received a methodological solution that can fit large-scale projects, which commonly include a great number of requirements. This paper proposes a semi-automated approach, which combines natural language processing (NLP) to identify ambiguous terms and statements and a soft computing technique to specify these terms using fuzzy set theory. This work contributes to the current literature on requirement specification by highlighting a line of research which paves the way to leverage the applications of advanced tools to allow the clarification of ambiguous requirements in large-scale projects.

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http://hdl.handle.net/10453/143567