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.

翻译：尽管持续集成（CI）流水线（或称构建）具有无可争议的优势，但其在耗时过长、失败和结果不稳定方面仍存在显著挑战。先前的研究往往孤立地处理CI中的问题，然而这些问题相互关联，需要采用整体性方法以实现有效优化。为弥合这一差距，本文提出了一种新颖构想：开发构建过程的数字孪生（DT），以实现全局性的持续改进。为支持这一构想，我们引入了CI构建过程数字孪生（CBDT）框架作为最小可行产品。该框架提供数字化映射功能，包括实时构建数据采集以及对构建过程性能指标的持续监控。此外，我们探讨了CBDT实际实施中的指导原则与挑战，包括：（1）利用机器学习对构建过程的不同方面进行建模；（2）基于历史模式探索假设性场景；（3）实施诸如自动化故障修复与性能修复等规范性服务，以持续改进构建过程。