Abstract
It is possible to build useful models for software project risk assessment based on Bayesian networks. A number of such models have been published and used and they provide valuable predictions for decision-makers. However, the accuracy of the published models is limited due to the fact that they are based on crudely discretised numeric nodes. In traditional Bayesian network tools such discretisation was inevitable; modelers had to decide in advance how to split a numeric range into appropriate intervals taking account of the trade-off between model efficiency and accuracy. However, recent a recent breakthrough algorithm now makes dynamic discretisation practical. We apply this algorithm to existing software project risk models. We compare the accuracy of predictions and calculation time for models with and without dynamic discretisation nodes.
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References
Agena, AgenaRisk User Manual, 2005
Agena, Software Project Risk Models Manual, Ver. 01.00, 2004
Fenton N., Neil M., Marsh W., Hearty P., Krause P., Mishra R. Predicting Software Defects in Varying Development Lifecycles using Bayesian Nets, to appear Information and Soft ware Technology, 2006
MODIST BN models, http://www.modist.org.uk/docs/modist_bn_models.pdf
Neil M., Tailor M., Marquez D., Bayesian statistical inference using dynamic discretisation, RADAR Technical Report, 2005
Neil M., Tailor M., Marquez D., Fenton N., Hearty P., Modelling Dependable Systems using Hybrid Bayesian Networks, Proc. of First International Conference on Availability, Re liability and Security (ARES 2006), 20–22 April 2006, Vienna, Austria
Neil M., Tailor M., Marquez D., Inference in Hybrid Bayesian Networks using dynamic discretisation, RADAR Technical Report, 2005
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© 2006 International Federation for Information Processing
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Fenton, N., Radliński, Ł., Neil, M. (2006). Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation. In: Sacha, K. (eds) Software Engineering Techniques: Design for Quality. IFIP International Federation for Information Processing, vol 227. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39388-9_14
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DOI: https://doi.org/10.1007/978-0-387-39388-9_14
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-39387-2
Online ISBN: 978-0-387-39388-9
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