More and more systems become software-intensive and this is in particular the case for cyber-physical systems, which are characterized by a close interaction between computational entities that control physical entities in the real world. Companies developing cyber-physical systems face an increasing difficulty in delivering high-quality solutions fulfilling customer demands within cost and delivery time, as the market requires faster and faster innovation. Current methods for software engineering are insufficient to meet this challenge.
Future state projection
Software-intensive systems, and in particular cyber-physical systems, will in the future be significantly more flexible, adaptable, and will require continuous evolution to instantaneously provide improved and new features to their users. An efficient and informed selection of existing components and services, and fast architectural adaptations will be crucial for companies’ success, which in turn requires a systematic approach in the decision-making process with respect to strategic component or service selection from a mixture of different options: in-house development, subcontracting, open source, COTS or services.
The research in the field has thus far been fragmented with researchers studying different aspects of component and service selection. ORION will bring a holistic decision support system to manage the trade-off between functionality, time to market, cost, quality and risk to develop competitive software-intensive system using components and services.
This will be done with respect to development of software-intensive systems, and in particular cyber-physical systems. The intention is to take strategic, tactical, and operational concerns into account. Specifically, ORION will address component or service selection and their integration into a system. It will address decision-making principles, methods, technologies and business models, and hence answer to industry needs and requirements. Five decision areas will be in focus from a software engineering perspective:
1) business and requirements engineering,
2) non-functional properties,
3) life-cycle perspective,
4) architecture, and
5) implementation and integration, including verification & validation (V&V).
Decision support based on evidence and a knowledge repository will complement the decision support system. An integration of the decision support system with a user interface will be provided, and used for empirical validation using an automotive experimental platform.
Open an overview presentation:
Decision-Support for Component-Based
Software Engineering of Cyber-Physical Systems