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

翻译：现代敏捷软件项目处于持续变化之中，这使得在整个项目生命周期中重新评估整体延期风险变得至关重要。现有的工作量估算模型是静态的，无法纳入项目执行过程中发生的变化。本文提出了一种利用延期模式和贝叶斯建模来持续预测整体延期的动态模型。该模型整合了项目阶段上下文，并从团队绩效随时间的变化中学习。我们将该方法应用于ING公司4040个史诗级任务和270个团队的真实数据。实证评估及与现有方法的对比表明，该方法在预测准确性上有显著提升。该动态模型始终优于静态方法和现有技术，即使在项目早期阶段也是如此。