标题： 从大爆炸集成中获得的教训：边缘计算和机器学习中的挑战 显示英文标题

标题： Lessons from a Big-Bang Integration: Challenges in Edge Computing and Machine Learning

摘要： 本次经验报告分析了一个为期一年的项目，该项目旨在使用边缘计算和机器学习构建一个分布式实时分析系统。 由于采用了大爆炸集成方法，即由多个地理位置分散的合作伙伴开发的所有组件在最终阶段合并，该项目遭遇了关键性挫折。 集成工作仅实现了六分钟的系统功能，远低于预期的四十分钟。 通过根本原因分析，该研究识别出技术和组织障碍，包括沟通不畅、缺乏早期集成测试以及对自上而下计划的抵触。 它还考虑了心理因素，例如更倾向于完全开发的组件而非原型。 本文提倡采用早期基于原型的部署、强大的通信基础设施，并采用自上而下的思维方式来管理复杂性并减少反应式分布式项目中的风险。 这些发现强调了传统敏捷方法在这种情况下的局限性，并提出了以仿真驱动的工程和结构化集成周期作为未来成功的关键促成因素。 显示英文摘要

摘要： This experience report analyses a one year project focused on building a distributed real-time analytics system using edge computing and machine learning. The project faced critical setbacks due to a big-bang integration approach, where all components developed by multiple geographically dispersed partners were merged at the final stage. The integration effort resulted in only six minutes of system functionality, far below the expected 40 minutes. Through root cause analysis, the study identifies technical and organisational barriers, including poor communication, lack of early integration testing, and resistance to topdown planning. It also considers psychological factors such as a bias toward fully developed components over mockups. The paper advocates for early mock based deployment, robust communication infrastructures, and the adoption of topdown thinking to manage complexity and reduce risk in reactive, distributed projects. These findings underscore the limitations of traditional Agile methods in such contexts and propose simulation-driven engineering and structured integration cycles as key enablers for future success.