Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral, or agile models when building ML-enabled systems. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary collaborations when building products with ML components. Based on the propositions, we found that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems. We recommend future research to investigate new process models, frameworks and tools that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation & Verification (V&V) for building ML-enabled systems.

翻译：机器学习（ML）组件正被越来越多地集成到关键且影响力巨大的软件系统中。然而，从原型ML模型到实际生产系统的软件开发过程依然面临挑战，其复杂性加剧，并需应对跨学科协作难题。这使得在构建ML赋能系统时，瀑布模型、螺旋模型或敏捷模型等传统软件生命周期模型的适用性受到影响。本研究从系统工程视角出发，探讨V模型在解决构建ML赋能系统时跨学科协作挑战中的应用。通过对软件企业从业者的访谈，我们建立了8项关于使用V模型管理含有ML组件产品构建过程中跨学科协作的基本命题。基于这些命题，我们发现尽管V模型需额外投入，但其特性能够有效应对从业者在构建ML赋能系统时遇到的若干协作挑战。我们建议未来研究探索利用V模型特性（例如系统分解、明确的系统边界以及验证与确认（V&V）的一致性）的新型过程模型、框架及工具，以支持ML赋能系统的构建。