  Machine learning (ML) enabled systems are emerging with recent breakthroughs
in ML. A model-centric view is widely taken by the literature to focus only on
the analysis of ML models. However, only a small body of work takes a system
view that looks at how ML components work with the system and how they affect
software engineering for MLenabled systems. In this paper, we adopt this system
view, and conduct a case study on Rasa 3.0, an industrial dialogue system that
has been widely adopted by various companies around the world. Our goal is to
characterize the complexity of such a largescale ML-enabled system and to
understand the impact of the complexity on testing. Our study reveals practical
implications for software engineering for ML-enabled systems.
