Quality trade-offs in ML-enabled systems: a multiple-case study
Vladislav Indykov, Rebekka Wohlrab, Daniel Strüber · 2025
When building a machine-learning-enabled system, quality objectives are achieved through architectural and non-architectural tactics, including general ones as well as specific ones that address machine learning specifics, such as the focus on data. However, implementing these tactics typically compromises other quality attributes that are not the primary focus of the tactic at hand. Previous research has investigated quality aspects and tactics for machine-learning-enabled systems, but there is a lack of detailed insights on quality trade-offs observed in industrial practice, and how companies address them. A study in this direction could especially help start-ups and SMEs to benefit from the insights of other companies, and academics to develop improved tactics addressing these trade-offs in alternative, potentially more effective ways.