Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method

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

Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method

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

Sharpe, K

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Fuzzy Sets and Systems, 2020, 383, pp. 127-145
Issue Date:: 2020-03-15

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Field	Value	Language
dc.contributor.author	Asadabadi, MR
dc.contributor.author	Chang, E
dc.contributor.author	Zwikael, O
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Sharpe, K
dc.date.accessioned	2021-01-19T03:31:01Z
dc.date.available	2021-01-19T03:31:01Z
dc.date.issued	2020-03-15
dc.identifier.citation	Fuzzy Sets and Systems, 2020, 383, pp. 127-145
dc.identifier.issn	0165-0114
dc.identifier.issn	1872-6801
dc.identifier.uri	http://hdl.handle.net/10453/145479
dc.description.abstract	© 2019 Elsevier B.V. The requirement specification process is an important part of a project and has the potential to prevent problems that may last for years after a project is delivered. Previous studies on the requirement specification process have focused on clarifying stated fuzzy terms in software requirement engineering. However, in many projects there is information that is not stated, but it is implied and can be inferred. This hidden information is usually ignored due to the assumption that ‘the provider understands what they mean/need’. This assumption is not always true. Such information, if extracted, may include fuzzy terms, namely hidden fuzzy terms (HFTs), which need specification. Therefore, these fuzzy terms have to be identified and then specified to avoid potential future consequences. This study proposes an algorithm to extract the hidden fuzzy terms, utilises a fuzzy inference system (FIS) to specify them, and applies the best worst multi-criteria decision making method (BWM) to evaluate the delivered product and measure the performance of the provider. The model is then used to examine a case from Defence Housing Australia. Such evaluation and measurement enable the project owner/manager to have a transparent basis to support decisions later in different phases of the project, and to ultimately reduce the likelihood of conflict and the receipt of an unsatisfactory product.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Fuzzy Sets and Systems
dc.relation.isbasedon	10.1016/j.fss.2019.06.017
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0101 Pure Mathematics, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method
dc.type	Journal Article
utslib.citation.volume	383
utslib.for	0101 Pure Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0101 Pure Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
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	*
pubs.consider-herdc	true
dc.date.updated	2021-01-19T03:30:36Z
pubs.publication-status	Published
pubs.volume	383

Abstract:

© 2019 Elsevier B.V. The requirement specification process is an important part of a project and has the potential to prevent problems that may last for years after a project is delivered. Previous studies on the requirement specification process have focused on clarifying stated fuzzy terms in software requirement engineering. However, in many projects there is information that is not stated, but it is implied and can be inferred. This hidden information is usually ignored due to the assumption that ‘the provider understands what they mean/need’. This assumption is not always true. Such information, if extracted, may include fuzzy terms, namely hidden fuzzy terms (HFTs), which need specification. Therefore, these fuzzy terms have to be identified and then specified to avoid potential future consequences. This study proposes an algorithm to extract the hidden fuzzy terms, utilises a fuzzy inference system (FIS) to specify them, and applies the best worst multi-criteria decision making method (BWM) to evaluate the delivered product and measure the performance of the provider. The model is then used to examine a case from Defence Housing Australia. Such evaluation and measurement enable the project owner/manager to have a transparent basis to support decisions later in different phases of the project, and to ultimately reduce the likelihood of conflict and the receipt of an unsatisfactory product.

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http://hdl.handle.net/10453/145479