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，因此在整个项目生命周期中重新评估整体延迟风险至关重要。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。本文提出一种利用延迟模式和贝叶斯建模持续预测整体延迟的动态模型。该模型整合了项目阶段的情景信息，并能从团队绩效随时间的演变中学习。我们将该方法应用于ING公司4,040个epics和270个团队的实际数据。实验评估以及与现有最先进方法的比较表明，该方法在预测准确性上取得显著提升。该动态模型在早期项目阶段即持续优于静态方法与现有最先进技术。