Multi-objectives Refinement of AADL Models for the Synthesis Embedded Systems (mu-RAMSES)

Model transformation has become now well established as an approach to control and automate the production of the software targeted at large or embedded systems. However, this approach still lacks the ability to be fully automated and to take into account the possibly very large number of Non Functional properties (NFPs) required by the system. Starting from a design written in an architecture description language (AADL), a large number of valid transformations are candidates to be applied, with the aim to refine this design, in a step wise manner, towards its implementation. These transformations may be interdependent, and their selection should take the complex dependency relation into account. The selection should also take into account the impact on NFPs, especially knowing that NFPs may very often be in conflict. In this paper, we propose an approach that automates (i) the identification of model transformation alternatives (MTAs) taking into account their dependencies, and (ii) the selection of MTAs, based on evolutionary algorithms (EAs), that produce the best output models with respect to NFPs. Experiments on a case study provide evidence that the approach can be successfully applied for code generation of real time embedded applications.