  Several approaches have recently used automated techniques to generate
architecture design alternatives by means of optimization techniques. These
approaches aim at improving an initial architecture with respect to quality
aspects, such as performance, reliability, or maintainability. In this context,
each optimization experiment usually produces a different set of architecture
alternatives that is characterized by specific settings. As a consequence, the
designer is left with the task of comparing such sets to identify the settings
that lead to better solution sets for the problem. To assess the quality of
solution sets, multi-objective optimization commonly relies on quality
indicators. Among these, the quality indicator for the maximum spread estimates
the diversity of the generated alternatives, providing a measure of how much of
the solution space has been explored. However, the maximum spread indicator is
computed only on the objective space and does not consider architectural
information (e.g., components structure, design decisions) from the
architectural space. In this paper, we propose a quality indicator for the
spread that assesses the diversity of alternatives by taking into account
architectural features. To compute the spread, we rely on a notion of distance
between alternatives according to the way they were generated during the
optimization. We demonstrate how our architectural quality indicator can be
applied to a dataset from the literature.
