Supporting Early Architectural Decision-Making through Tradeoff Analysis: A Study with Volvo Cars

Paper i proceeding, 2024

As automotive companies increasingly move operations to the cloud, they need to carefully make architectural decisions. Currently, architectural decisions are made ad-hoc and depend on the experience of the involved architects. Recent research has proposed the use of data-driven techniques that help humans to understand complex design spaces and make thought-through decisions. This paper presents a design science study in which we explored the use of such techniques in collaboration with architects at Volvo Cars. We show how a software architecture can be simulated to make more principled design decisions and allow for architectural tradeoff analysis. Concretely, we apply machine learning-based techniques such as Principal Component Analysis, Decision Tree Learning, and clustering. Our findings show that the tradeoff analysis performed on the data from simulated architectures gave important insights into what the key tradeoffs are and what design decisions shall be taken early on to arrive at a high-quality architecture.