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dc.contributor.authorvan Koten, Chikakoen_NZ
dc.contributor.authorGray, Andrewen_NZ
dc.date.available2011-04-07T03:06:14Z
dc.date.copyright2005-11en_NZ
dc.identifier.citationvan Koten, C., & Gray, A. (2005). Bayesian statistical effort prediction models for data-centred 4GL software development (Information Science Discussion Papers Series No. 2005/09). University of Otago. Retrieved from http://hdl.handle.net/10523/1042en
dc.identifier.urihttp://hdl.handle.net/10523/1042
dc.description.abstractConstructing an accurate effort prediction model is a challenge in Software Engineering. This paper presents three Bayesian statistical software effort prediction models for database-oriented software systems, which are developed using a specific 4GL tool suite. The models consist of specification-based software size metrics and development team’s productivity metric. The models are constructed based on the subjective knowledge of human expert and calibrated using empirical data collected from 17 software systems developed in the target environment. The models’ predictive accuracy is evaluated using subsets of the same data, which were not used for the models’ calibration. The results show that the models have achieved very good predictive accuracy in terms of MMRE and pred measures. Hence it is confirmed that the Bayesian statistical models can predict effort successfully in the target environment. In comparison with commonly used multiple linear regression models, the Bayesian statistical models’ predictive accuracy is equivalent in general. However, when the number of software systems used for the models’ calibration becomes smaller than five, the predictive accuracy of the best Bayesian statistical models are significantly better than the multiple linear regression model. This result suggests that the Bayesian statistical models would be a better choice when software organizations/practitioners do not posses sufficient empirical data for the models’ calibration. The authors expect those findings encourage more researchers to investigate the use of Bayesian statistical models for predicting software effort.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjecteffort predictionen_NZ
dc.subject4GLen_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 effort prediction models for data-centred 4GL software developmenten_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages30en_NZ
otago.date.accession2005-12-02en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints24en_NZ
otago.school.eprintsSoftware Metrics Research Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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otago.relation.number2005/09en_NZ
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