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In model driven-engineering, there is a myriad of approaches that use models and transformations to develop software systems. However, a few works in the literature have discussed the simplification of these models to make them more readable, understandable, and easy to navigate. This article proposed an approach that simplifies models in order to assist architects in controlling architecture evolution and quality, especially with large systems. This approach consists of two main concepts, multi-view modeling and on-demand model projection. In the former, formally specified models are divided into two views. The first one is dedicated to describe quality attributes. The second view represents the architectural view. The multi-viewing can enrich stakeholder reasoning about the developed architecture and simplify the mapping between quality attributes and architectural decisions at different abstraction levels. In the latter, the projection concept consists of extracting from source models only elements of interest to generate simpler and narrower models as output.

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Managing software architecture represents a big challenge throughout the development lifecycle. The complexity of the involved structural elements and the relations among them make the specified models look oversized and fuzzy, which makes the architecture incomprehensible, hard to maintain, and difficult to assess its quality. This paper's goal is to propose a powerful methodology for simplifying and reducing models' complexity to increase understandability, smoothing maintenance, and facilitating architecture monitoring and assessment. For this purpose, the authors rely heavily on two major concepts, multi-view modeling, and incremental model projection. The multi-viewing requires that all models must have two main views to describe the architecture and the mapping to its relevant quality attributes. The incremental projection is a methodology used to specialize and minimize models progressively to make them simpler and clearer. The results show that projecting models incrementally can reduce and narrow them significantly.