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 model when building ML-enabled systems. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the collaboration challenges when building ML-enabled systems. By interviewing with practitioners from software companies, we established a set of 8 propositions for using V-Model to handle interdisciplinary collaboration challenges 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.

Jie “JW” Wu is currently a Postdoctoral Research Fellow at the University of British Columbia, working at the intersection of Software Engineering and AI. He got his PhD in Systems Engineering at George Washington University. He received his M.Sc. degree and B.Sc. degree (ACM Class, an elite CS program) in Computer Science at Shanghai Jiao Tong University. He worked as a software engineer in the industry for nearly a decade at Snap Inc., Microsoft, and startup (ArcSite).