The rising demand for adaptive, cloud-based and AI-based systems is calling for an upgrade of the associated dependability concepts. That demands instantiation of dependability-orientated processes and methods to cover the whole life cycle. However, a common solution is not in sight yet, especially evident for continuously learning AI and/or dynamic runtime-based approaches. In this context, the TEACHING engineering methods and design patterns can support the development of dependable AI-based autonomous systems and/or dynamic runtime-based systems. As such, the body of knowledge and engineering use-cases refined by the TEACHING project can provide the industry with latest state-of-the-art approaches and provide support for development of the systems according to the best knowledge. The dependable engineering methods consider architectural concepts and their applicability to different scenarios to ensure the dependability of evolving AI-based CPSoS. The modular engineering approaches can be tailored for the specific need of the individual industry and provide good practices to speed up development.
Innovation capacity: The dependable engineering methods consider architectural concepts, proven good practices and can be tailored for the different application scenarios of evolving AI-based CPSoS. The modular engineering approaches can be tailored for the specific need of the individual industry and provide good practices to speed up development. The methods, pattern, and approaches represent the latest state-of-the-art and provide thus evidence for the engineering according to SotA.
