When designing a software system, architects make a series of design
decisions that directly impact the system's quality. The number of available
design alternatives grows rapidly with system size, creating an enormous
space of intertwined design concerns that renders manual exploration
impractical. We present eQual, a model-driven technique for simulation-based
assessment of architectural designs. While it is not possible to guarantee
optimal decisions so early in the design process, eQual improves decision
quality. eQual is effective in practice because it (1) limits the amount of
information the architects have to provide and (2) adapts optimization
algorithms to effectively explore massive spaces of design alternatives. We
empirically demonstrate that eQual yields designs whose quality is comparable
to a set of systems' known optimal designs. A user study shows that, compared
to the state-of-the-art, engineers using eQual produce statistically
significantly higher-quality designs with a large effect size, are
statistically significantly more confident in their designs, and find eQual
easier to use.