Please use this identifier to cite or link to this item: https://doi.org/10.1109/PRDC.2008.21
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dc.titleA bayesian inference approach for probabilistic analogy based software maintenance effort estimation
dc.contributor.authorLi, Y.F.
dc.contributor.authorXie, M.
dc.contributor.authorGoh, T.N.
dc.date.accessioned2014-06-19T04:52:31Z
dc.date.available2014-06-19T04:52:31Z
dc.date.issued2008
dc.identifier.citationLi, Y.F.,Xie, M.,Goh, T.N. (2008). A bayesian inference approach for probabilistic analogy based software maintenance effort estimation. Proceedings of the 14th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2008 : 176-183. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/PRDC.2008.21" target="_blank">https://doi.org/10.1109/PRDC.2008.21</a>
dc.identifier.isbn9780769534480
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72226
dc.description.abstractSoftware maintenance effort estimation is essential for the success of software maintenance process. In the past decades, many methods have been proposed for maintenance effort estimation. However, most existing estimation methods only produce point predictions. Due to the inherent uncertainties and complexities in the maintenance process, the accurate point estimates are often obtained with great difficulties. Therefore some prior studies have been focusing on probabilistic predictions. Analogy Based Estimation (ABE) is one popular point estimation technique. This method is widely accepted due to its conceptual simplicity and empirical competitiveness. However, there is still a lack of probabilistic framework for ABE model. In this study, we first propose a probabilistic framework of ABE (PABE). The predictive PABE is obtained by integrating over its parameter k number of nearest neighbors via Bayesian inference. In addition, PABE is validated on four maintenance datasets with comparisons against other established effort estimation techniques. The promising results show that PABE could largely improve the point estimations of ABE and achieve quality probabilistic predictions. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/PRDC.2008.21
dc.sourceScopus
dc.subjectBayesian inference
dc.subjectk-nearest neighbors
dc.subjectProbabilistic analogy based model
dc.subjectSoftware maintenance
dc.subjectSoftware maintenance effort estimation
dc.typeConference Paper
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.description.doi10.1109/PRDC.2008.21
dc.description.sourcetitleProceedings of the 14th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2008
dc.description.page176-183
dc.identifier.isiutNOT_IN_WOS
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