The transition from isolated microcontrollers, designed for singular functionalities, to sophisticated microprocessors capable of supporting multiple applications concurrently marks a significant evolution in technology. This development presents particular challenges within the context of functional safety, especially the issue of mixed criticality, which involves identification and mitigation of unwanted interactions between applications with varying safety integrity levels. Traditionally, this problem has been addressed through strict isolation of individual applications, such as by employing hypervisor technologies or utilizing operating-system partitioning mechanisms. However, these approaches can be misapplied, particularly in agile development environments for household applications, where systems are constantly evolving. Therefore, we propose a systematic and automated method for identifying interference that involves modeling relevant information about embedded hardware and software design. To illustrate our approach, we provided a real-world example of a household device.

Trust is a fundamental aspect in enabling self-adaptation of intelligent systems and in paving the way towards a smooth adoption of technological innovations in our societies. While Artificial Intelligence (AI) is capable to uplift the human contribution to our societies while protecting environmental resources, its ethical and technical trust dimensions bring significant challenges for a sustainable self-adaptive evolution in the domain of safety-critical systems. Inspired from the safety assurance case, in this paper we introduce the concept of trust assurance case together with the implementation of its ethical and technical principles directed towards assuring a trustworthy sustainable evolution of safety-critical AI-controlled systems.

Digital transformation fundamentally changes established practices in public and private sector. Hence, it represents an opportunity to improve the value creation processes (e.g., “industry 4.0”) and to rethink how to address customers' needs such as “data-driven business models” and “Mobility-as-a-Service”. Dependable, collaborative and autonomous systems are playing a central role in this transformation process. Furthermore, the emergence of data-driven approaches combined with autonomous systems will lead to new business models and market dynamics. Innovative approaches to reorganise the value creation ecosystem, to enable distributed engineering of dependable systems and to answer urgent questions such as liability will be required. Consequently, digital transformation requires a comprehensive multi-stakeholder approach which properly balances technology, ecosystem and business innovation. Targets of this paper are (a) to introduce digital transformation and the role of / opportunities provided by autonomous systems, (b) to introduce Digital Depednability Identities (DDI) - a technology for dependability engineering of collaborative, autonomous CPS, and (c) to propose an appropriate agile approach for innovation management based on business model innovation and co-entrepreneurship.

Art. 5381856, 2 S.

Since April 2014 the Artemis/ECSEL project EMC2 is running and provides significant results. EMC2 stands for "Embedded Multi-Core Systems for Mixed Criticality Applications in Dynamic and Changeable Real-Time Environments". In this paper we report recent progress on technical work in the different workpackages and use cases. We highlight progress in the research on system architecture, design methodology, platform and operating systems, and in qualification and certification. Application cases in the fields of automotive, avionics, health care, and industry are presented exploiting the technical results achieved.

Cyber-Physical Systems (CPS) provide enormous potential for innovation but a precondition for this is that the issue of dependability has been addressed. This paper presents the concept of a Digital Dependability Identity (DDI) of a component or system as foundation for assuring the dependability of CPS. A DDI is an analyzable and potentially executable model of information about the dependability of a component or system. We argue that DDIs must fulfill a number of properties including being universally useful across supply chains, enabling off-line certification of systems where possible, and providing capabilities for in-field certification of safety of CPS. In this paper, we focus on system safety as one integral part of dependability and as a practical demonstration of the concept, we present an initial implementation of DDIs in the form of Conditional Safety Certificates (also known as ConSerts). We explain ConSerts and their practical operationalization based on an illustrative example.

In recent years it has become more and more evident that openness and adaptivity are key characteristics of next-generation distributed systems. The reason for this is not least due to the advent of computing trends like ubiquitous computing, ambient intelligence, and cyber-physical systems, where systems are usually open for dynamic integration and able to react adaptively to changing situations. Despite being open and adaptive, it is a common requirement for such systems to be safe. However, traditional safety assurance techniques, both state-of-the-practice and state-of-the-art ones, are not sufficient in this context. We have recently developed some initial solution concepts based on conditional safety certificates and corresponding runtime analyses. In this article we show how to operationalize these concepts. To this end, we present in detail how to specify conditional safety certificates, how to transform them into suitable runtime models, and how these models finally support dynamic safety evaluations.

Resilience has been successfully realized in automotive systems to increase system reliability at reasonable costs. Using dynamic adaptation, the system adapts to runtime errors - caused by internal system faults or adverse environmental situations like critical driving situations - in order to provide the best possible functionality and to guarantee system safety in any given system and environmental state. This paper introduces an engineering approach for developing resilient systems using dynamic adaptation. The approach is based on component-oriented modeling and on analyses of component compositions. We describe how component-oriented modeling and compositional analyses enable the usage of dynamic adaptation for achieving a trade-off between availability and cost in safety-critical, resilient systems and how it helps to manage the complexity inherent in component composition.

With the advent of new computing paradigms, such as Ubiquitous Computing, Ambient Intelligence, and Cyber Physical Systems, promising application domains like Ambient Assisted Living (AAL) and Car2Car have emerged. One key concern in these application domains is that systems are required to dynamically adapt in reaction to changes within the system or its environment. As a consequence, it is indispensable to incorporate corresponding facilities within systems, yielding adequate adaptation behavior to appropriately react on such dynamic changes. The definition of adaptation behavior is, however, a complex task in its own. In order to help engineers working on adaptive systems, we propose in this paper a generic testbed that allows specifying adaptation strategies and evaluating them in a runtime context to improve the adaptation behavior. Our approach further introduces basic visualization features as a means for the engineers to better evaluate the complex dynamic behavior resulting from runtime adaptation.

This article presents a novel approach to facilitating fault tree analysis during the development of software-controlled systems. Based on a component-oriented system model, it combines second-order probabilistic analysis and automatically generated default failure models with a level-of-detail concept to ensure early and continuous analysability of system failure behaviour with optimal effort, even in the presence of incomplete information and dissimilar levels of detail in different parts of an evolving system model. The viability and validity of the method are demonstrated by means of an experiment.

Upcoming application domains, from ambient assisted living to car2car, show the need for openness, flexibility and safety in next generation embedded systems. Whilst there are several approaches tackling the dynamic reconfiguration and integration of components, there has not been much research done on safety of such systems. As many of the application domains are inherently safety critical this hinders open systems of systems to unfold their full potential. Models at runtime have shown to foster dynamic adaptation of software systems. In a similar way, the integration of appropriate runtime safety models and dynamic evaluation mechanisms into systems seems to be a viable approach to enable safety management at runtime. In this paper we sketch out our modeling approach for adaptive ad hoc systems and present first results with respect to the integration and usage of safety models at runtime.