一句话：提出结合延期模式与贝叶斯建模的动态模型，持续预测敏捷软件项目总体延期风险，并在 ING 真实数据上显著优于静态方法。
敏捷软件项目持续变更，静态工作量估算难以纳入执行中的变化，因此需要在项目生命周期内持续评估总体延期风险。本文提出一种动态预测模型，利用延期模式（delay patterns）与贝叶斯建模，结合项目阶段上下文，并随时间学习团队绩效变化，从而持续预测总体延期。作者将该方法应用于 ING 的 4,040 个 epic 和 270 个团队的真实数据，并与静态方法和 state-of-the-art 进行比较。评估表明，动态模型显著提升预测准确率，在整个项目周期内稳定优于静态方法，甚至在项目早期阶段也表现更好。
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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{KulaGDG23,
title = {Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling},
author = {Elvan Kula and Eric Greuter and Arie van Deursen and Georgios Gousios},
booktitle = {FSE},
year = {2023},
doi = {10.1145/3611643.3616328}
}