An Exploratory Study of V-Model in Building ML-Enabled Software: A Systems Engineering Perspective

TL;DR

This study explores how the V-Model, a systems engineering process, can address interdisciplinary collaboration challenges in building ML-enabled software systems, highlighting its potential benefits and areas for future research.

Contribution

It applies a systems engineering perspective to evaluate V-Model's effectiveness in managing interdisciplinary collaboration in ML-enabled system development.

Findings

V-Model aligns with collaboration challenges in ML systems

Despite extra efforts, V-Model characteristics support effective collaboration

Recommendations for future research on process models leveraging V-Model features

Abstract

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.