The Architect in the Maze: On the Effective Usage of Automated Design Exploration

Designing a software architecture that satisfies a set of quality-attribute requirements has traditionally been a challenging activity for human architects, as it involves the exploration and assessment of alternative design decisions. The development of automated optimization tools for the architecture domain has opened new opportunities, because these tools are able to explore a large space of alternatives, and thus extend the architect’s capabilities. In this context, however, architects need to efficiently navigate through a large space and understand the main relations between design decisions and feasible quality-attribute tradeoffs in a maze of possible alternatives. Although Machine Learning (ML) techniques can help to reduce the complexity of the task by sifting through the data generated by the tools, the standard techniques often fall short because they cannot offer architectural insights or relevant answers to the architect’s questions. In this paper, and based on previous experiences, we argue that ML techniques should be adapted to the architecture domain, and propose a conceptual framework towards that goal. Furthermore, we show how the framework can be instantiated by adapting clustering techniques to answer architectural questions regarding a client-server design space.