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dc.contributor.authorvan Koten, Chikakoen_NZ
dc.date.available2011-04-07T03:16:22Z
dc.date.copyright2007-05en_NZ
dc.identifier.citationvan Koten, C. (2007, May). A comparison of software effort prediction models using small datasets. University of Otago.en
dc.identifier.urihttp://hdl.handle.net/10523/1461
dc.descriptionSubmitted to IEEE Transactions on Software Engineering. If published, this version will be replaced by the final version.en_NZ
dc.description.abstractConstructing an accurate effort prediction model is a challenge in Software Engineering. One difficulty practitioners often experience is that they have only a very small amount of local data to construct a model. The small dataset limits predictive accuracy of the model, since the accuracy deteriorates as the size of the dataset decreases. This paper compares three different software development effort prediction models that are applicable to these small datasets. They are: (1) Bayesian statistical models, (2) multiple linear regression models and (3) case-based reasoning/analogy-based models. The predictive accuracy of these models is evaluated using two different software datasets. The results have shown that the accuracy of the Bayesian statistical models is higher than or competitive with that of the others, when calibrated using data collected from fewer than 10 systems. These suggest that the Bayesian statistical model would be a better choice in effort prediction when the practitioners have only a very small dataset, consisting of fewer than 10 systems similar to their system of interest.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.subjectmultivariate statisticsen_NZ
dc.subjectmodeling methodologiesen_NZ
dc.subjectmanagement techniquesen_NZ
dc.subjectstatistical methodsen_NZ
dc.subjectcost estimationen_NZ
dc.subjecttime estimationen_NZ
dc.subject.lcshQA75 Electronic computers. Computer scienceen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleA comparison of software effort prediction models using small datasetsen_NZ
dc.typeOtheren_NZ
dc.description.versionSubmitteden_NZ
otago.bitstream.pages41en_NZ
otago.date.accession2007-05-08en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints688en_NZ
otago.school.eprintsSoftware Engineering & Collaborative Modelling Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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