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dc.contributor.authorGray, AR
dc.contributor.authorMacDonell, SG
dc.date.accessioned2011-08-30T04:55:58Z
dc.date.available2011-08-30T04:55:58Z
dc.date.copyright1999
dc.date.issued2011-08-30
dc.identifier.citationProceedings of the ICONIP'99/ANZIIS'99/ ANNES'99/ACNN'99 International Workshop on Future Directions for Intelligent Systems and Information Sciences, Dunedin, New Zealand, Published proceedings: Full paper(E1), pp.235-240
dc.identifier.urihttp://hdl.handle.net/10292/1955
dc.description.abstractSoftware metrics are measurements of development processes, products, and resources. Once these measurements have been specified and collected they can be used as variables in empirically calibrated models for a wide range of project management purposes; including the task of predicting development effort based on some combination of size, complexity, and developer experience metrics. One difficulty encountered when using traditional algorithmic approaches to estimation has been the collection of the appropriate metrics required to use the model in its predictive capacity. Project managers are generally unable to make precise quantitative estimates for the independent variables, especially early in system development when these models are at their most valuable. The alternative of using qualitative values for the inputs, as in fuzzy logic, has been suggested but the stability and consistency of such labels has yet to be established, as well as considering the elicitation techniques available for deriving the membership functions. In this paper we examine the perceptions of data model size, functionality size, developer experience, and project effort in terms of three fuzzy membership functions from two separate surveys of project managers, each with a very different approach. The consistency of results across the two surveys is examined, and some discussion about the strengths and weaknesses of the two approaches is provided.
dc.publisherUniversity of Otago
dc.relation.urihttp://www.otago.ac.nz/researchpublications/?command=Search&sAOUname=Information%20Science&sPublicationyear_to=1999
dc.rightsNOTICE: 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.titleMembership function extraction from software development project managers
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess


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