Abstract

We propo se an applied Bayesian learning approach for continuous planning and evolution of information system projects and portfolios. Unlike traditional project management approaches for information system, the proposed approach considers the cumulative effect of all past experiences to achieve continuous performance and reliability prediction. The results of quantitative comparisons with other common estimation approaches, such as non-learning point estimates and traditional Bayesian approach, using real case data indicate that the proposed approach can generate a more realistic metric to continuously plan and measure the performance of evolving LeAgile projects or portfolios. This study can support decision makers, engineering teams, and management by supplying a practical and scalable project performance prediction tool for continuous planning and system evolution.

摘要

我们提出了一种应用贝叶斯学习方法，用于信息系统项目和投资组合的持续规划和发展。与传统的信息系统项目管理方法不同，该方法考虑了所有过去经验的累积效应，以实现持续的性能和可靠性预测。使用真实案例数据与其他常见估计方法（例如非学习点估计和传统贝叶斯方法）进行定量比较的结果表明，所提出的方法可以生成更现实的指标来持续规划和衡量不断发展的 LeAgile 项目或投资组合。这项研究可以为决策者、工程团队、