RM4ML: requirements model for machine learning-enabled software systems

Machine learning (ML)-enabled is one of the appealing characteristics of modern software systems, which usually contain ML components to make the system more intelligent for easier living. Requirements for ML-enabled software systems involve functional, quality, environmental, and data requirements. UML is a de facto approach for requirements analysis and system design, but its current modeling capabilities do not yet cover ML-enabled software systems to describe software quality requirements, environmental requirements, and data requirements. In this paper, we propose a requirements model for ML-enabled software systems and a modeling process for this model based on an extension of UML. In addition, we demonstrate the proposed model and modeling process through the case of the Tesla Autopilot system. The results show that the proposed model is expressive and usable and has a low learning curve when the software developers have basic knowledge of UML. Our proposed model can be further implemented and used in industrial settings.