用数字孪生技术优化构建流程，实现持续改进。
Towards Build Optimization Using Digital Twins
- 构建数字孪生系统实时追踪和监控构建过程
- 支持基于历史数据的假设分析与自动修复
- 适合研发团队提升构建稳定性与效率
尽管持续集成（CI）流水线（或构建）具有显著优势，但其仍面临耗时长、失败率高和不稳定性等问题。以往研究多孤立解决其中某一问题，而这些挑战相互关联，需整体优化。本文提出构建构建流程的数字孪生（CBDT）框架，作为最小可行产品，实现构建过程的实时数据采集与性能指标持续监控。该框架支持机器学习建模构建各环节、基于历史模式进行‘如果…会怎样’的假设分析，并提供自动化故障修复与性能优化等预测性服务，推动构建流程的全局持续改进。文中还讨论了实际部署中的关键指南与挑战。
原文摘要 · Abstract (English)
Despite the indisputable benefits of Continuous Integration (CI) pipelines (or builds), CI still presents significant challenges regarding long durations, failures, and flakiness. Prior studies addressed CI challenges in isolation, yet these issues are interrelated and require a holistic approach for effective optimization. To bridge this gap, this paper proposes a novel idea of developing Digital Twins (DTs) of build processes to enable global and continuous improvement. To support such an idea, we introduce the CI Build process Digital Twin (CBDT) framework as a minimum viable product. This framework offers digital shadowing functionalities, including real-time build data acquisition and continuous monitoring of build process performance metrics. Furthermore, we discuss guidelines and challenges in the practical implementation of CBDTs, including (1) modeling different aspects of the build process using Machine Learning, (2) exploring what-if scenarios based on historical patterns, and (3) implementing prescriptive services such as automated failure and performance repair to continuously improve build processes.
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