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Modern agile software projects are subject to constant change, making itessential to re-asses overall delay risk throughout the project life cycle.Existing effort estimation models are static and not able to incorporatechanges occurring during project execution. In this paper, we propose a dynamicmodel for continuously predicting overall delay using delay patterns andBayesian modeling. The model incorporates the context of the project phase andlearns from changes in team performance over time. We apply the approach toreal-world data from 4,040 epics and 270 teams at ING. An empirical evaluationof our approach and comparison to the state-of-the-art demonstrate significantimprovements in predictive accuracy. The dynamic model consistently outperformsstatic approaches and the state-of-the-art, even during early project phases.

Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling