Improving IT Change Management Processes with Automated Risk Assessment
DSOM '09 Proceedings of the 20th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Integrated Management of Systems, Services, Processes and People in IT
Defect cost flow model: a Bayesian network for predicting defect correction effort
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Computer Networks: The International Journal of Computer and Telecommunications Networking
Discretization methods for NBC in effort estimation: an empirical comparison based on ISBSG projects
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
A model to detect problems on scrum-based software development projects
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Hi-index | 0.00 |
Bayesian networks, which can combine sparse data, prior assumptions and expert judgment into a single causal model, have already been used to build software effort prediction models. We present such a model of an Extreme Programming environment and show how it can learn from project data in order to make quantitative effort predictions and risk assessments without requiring any additional metrics collection program. The model's predictions are validated against a real world industrial project, with which they are in good agreement.