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dc.contributor.authorMacDonell, Stephenen_NZ
dc.contributor.authorGray, Andrewen_NZ
dc.contributor.authorCalvert, Jamesen_NZ
dc.date.available2011-04-07T03:06:28Z
dc.date.copyright1999-06en_NZ
dc.identifier.citationMacDonell, S., Gray, A., & Calvert, J. (1999). FULSOME: Fuzzy logic for software metric practitioners and researchers (Information Science Discussion Papers Series No. 99/13). University of Otago. Retrieved from http://hdl.handle.net/10523/1088en
dc.identifier.urihttp://hdl.handle.net/10523/1088
dc.description.abstractThere has been increasing interest in recent times for using fuzzy logic techniques to represent software metric models, especially those predicting development effort. The use of fuzzy logic for this application area offers several advantages when compared to other commonly used techniques. These include the use of a single model with different levels of precision for inputs and outputs used throughout the development life cycle, the possibility of model development with little or no data, and its effectiveness when used as a communication tool. The use of fuzzy logic in any applied field however requires that suitable tools are available for both practitioners and researchers---satisfying both interface and functionality related requirements. After outlining some of the specific needs of the software metrics community, including results from a survey of software developers on this topic, the paper describes the use of a set of tools called FULSOME (Fuzzy Logic for Software Metrics). The development of a simple fuzzy logic system by a software metrician and subsequent tuning are then discussed using a real-world set of software metric data. The automatically generated fuzzy model performs acceptably when compared to regression-based models.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleFULSOME: Fuzzy logic for software metric practitioners and researchersen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages8en_NZ
otago.date.accession2010-11-10 20:33:14en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints997en_NZ
otago.school.eprintsSoftware Metrics Research Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.references[1] N. E. Fenton and S. L. Pfleeger. Software Metrics: A Rigorous & Practical Approach. PWS, 1997. [2] A. Gray and S. MacDonell. Applications of fuzzy logic to software metric models for development effort estimation. In Proceedings of the 1997 Annual meeting of the North American Fuzzy Information Processing Society - NAFIPS’97, pages 394–399. IEEE, 1997. [3] A. Gray and S. MacDonell. A comparison of model building techniques to develop predictive equations for software metrics. Information and Software Technology, 39:425–437, 1997. [4] A. Gray and S. MacDonell. Fuzzy logic for software metric models throughout the development life-cycle. In Proceedings of the 1999 Annual meeting of the North American Fuzzy Information Processing Society - NAFIPS’99. IEEE, 1999. [5] Y. Miyazaki, M. Terakado, K. Ozaki, and N. Nozaki. Robust regresison for developing software estimation models. Journal of System and Software, 27:35–16, 1994.en_NZ
otago.relation.number99/13en_NZ
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