M.: Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation, Software Engineering Techniques: Design for Quality

Abstract. It is possible to build useful models for software project risk assess-ment based on Bayesian networks. A number of such models have been pub-lished 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 ad-vance how to split a numeric range into appropriate intervals taking account of the trade-off between model efficiency and accuracy. However, recent break-through 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 discreti-sation nodes.