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dc.contributor.authorvan Koten, Chikakoen_NZ
dc.date.available2011-04-07T03:05:39Z
dc.date.copyright2005-10en_NZ
dc.identifier.citationvan Koten, C. (2005). Bayesian statistical models for predicting software development effort (Information Science Discussion Papers Series No. 2005/08). University of Otago. Retrieved from http://hdl.handle.net/10523/933en
dc.identifier.urihttp://hdl.handle.net/10523/933
dc.description.abstractConstructing an accurate effort prediction model is a challenge in Software Engineering. This paper presents new Bayesian statistical models, in order to predict development effort of software systems in the International Software Benchmarking Standards Group (ISBSG) dataset. The first model is a Bayesian linear regression (BR) model and the second model is a Bayesian multivariate normal distribution (BMVN) model. Both models are calibrated using subsets randomly sampled from the dataset. The models’ predictive accuracy is evaluated using other subsets, which consist of only the cases unknown to the models. The predictive accuracy is measured in terms of the absolute residuals and magnitude of relative error. They are compared with the corresponding linear regression models. The results show that the Bayesian models have predictive accuracy equivalent to the linear regression models, in general. However, the advantage of the Bayesian statistical models is that they do not require a calibration subset as large as the regression counterpart. In the case of the ISBSG dataset it is confirmed that the predictive accuracy of the Bayesian statistical models, in particular the BMVN model is significantly better than the linear regression model, when the calibration subset consists of only five or smaller number of software systems. This finding justifies the use of Bayesian statistical models in software effort prediction, in particular, when the system of interest has only a very small amount of historical data.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjecteffort predictionen_NZ
dc.subjectBayesian statisticsen_NZ
dc.subjectregressionen_NZ
dc.subjectsoftware metricsen_NZ
dc.subject.lcshQA75 Electronic computers. Computer scienceen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleBayesian statistical models for predicting software development efforten_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages27en_NZ
otago.date.accession2005-12-02en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints22en_NZ
otago.school.eprintsSoftware Metrics Research Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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otago.relation.number2005/08en_NZ
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