摘要：AbstractDesigners of today's embedded systems are faced with increasing complexity both in the applications and in the platforms they run on. The use of complex platforms means that the engineers need to make non-trivial and many times non-intuitive decisions during the design phase. This situation creates a need for better ways to manage project complexity, and Model Driven Engineering is a possible solution. In this work, we propose an extension of a Model Driven Engineering methodology (i.e. HIPA02) to help manage complexity, which includes an automated Design Space Exploration phase that uses Genetic Algorithms to find the best application-to-platform mapping. We discuss about HIPA02’s characteristics as an MD-DSE methodology, applying it to readily available benchmarks, and to randomly-generated application models.
