(1) Please use the same participant numbering as that used in Proposal submission forms A2
(2) For partners from ARTEMIS Member States, please indicate whether you consider that you comply with the national eligibility criteria for funding as stated in the document "Eligibility Criteria" published in the Call.
(copied from Part A)
The objective of the project is to develop a methodology and tool support for model-driven evolutionary development of complex embedded systems. Supporting evolutionary design will reduce the development time and time-to-market, reduce development and unit costs, increase the quality of the products and of the engineering processes and reduce re-certification costs.
Today, most systems are engineered in an evolutionary fashion: introducing a new version of an existing product, introducing new features—possibly as part of an evolution of a product line, performing a design-iteration, etc. The models of an embedded system will evolve at the same time with the system. However, none of the current state-of-the-art approaches to model-based engineering of embedded systems support evolutionary development.
The project deploys the model-based evolutionary development in domains with the following characteristics:
Longevity, addressing the concerns of high quality, product evolution, platform and low-cost maintenance
Tuning & Scaling, addressing the concerns of physical variations in production process, high configurability and need for calibration
Reliability & Safety, addressing the concerns of measurement & control, safety critical systems, real-time behaviour and compositional safety
We will extend the state-of-the-art modelling frameworks to capture the knowledge learned during evolutions, such as, when and how can a component be reused, performance metrics recorded during the runtime, design rationale for a design decision, time and effort required for different development phases. We will develop non-intrusive analysis and monitoring techniques to systematically collect information during the evolutions, and link this information to the models.
Often, embedded system architectures are derived with little concern for extensibility, rendering evolutions very costly. We will develop decision support methods and tools for the creation of system architectures that are extensible, but without compromising other objectives such as performance, cost, energy consumption, safety and dependability. The evolution of models is often done manually which is tedious and error prone, without any systematic model management. We will develop methods and tools for model management and visualization, to automate the management of models and improve comprehension during the system evolution.