As automation increases qualitatively and quantitatively in safety-critical human cyber-physical systems, it is becoming more and more challenging to increase the probability or ensure that human operators still perceive key artifacts and comprehend their roles in the system. In the companion paper, we proposed an abstract reference architecture capable of expressing all classes of system-level interactions in human cyber-physical systems. Here we demonstrate how this reference architecture supports the analysis of levels of communication between agents and helps to identify the potential for misunderstandings and misconceptions. We then develop a metamodel for safe human machine interaction. Therefore, we ask what type of information exchange must be supported on what level so that humans and systems can cooperate as a team, what is the criticality of exchanged information, what are timing requirements for such interactions, and how can we communicate highly critical information in a limited time frame in spite of the many sources of a distorted perception. We highlight shared stumbling blocks and illustrate shared design principles, which rest on established ontologies specific to particular application classes. In order to overcome the partial opacity of internal states of agents, we anticipate a key role of virtual twins of both human and technical cooperation partners for designing a suitable communication.

The design and analysis of multi-agent human cyber-physical systems in safety-critical or industry-critical domains calls for an adequate semantic foundation capable of exhaustively and rigorously describing all emergent effects in the joint dynamic behavior of the agents that are relevant to their safety and well-behavior. We present such a semantic foundation. This framework extends beyond previous approaches by extending the agent-local dynamic state beyond state components under direct control of the agent and belief about other agents (as previously suggested for understanding cooperative as well as rational behavior) to agent-local evidence and belief about the overall cooperative, competitive, or coopetitive game structure. We argue that this extension is necessary for rigorously analyzing systems of human cyber-physical systems because humans are known to employ cognitive replacement models of system dynamics that are both non-stationary and potentially incongruent. These replacement models induce visible and potentially harmful effects on their joint emergent behavior and the interaction with cyber-physical system components.

We propose a reference architecture of safety-critical or industry-critical human cyber-physical systems (CPSs) capable of expressing essential classes of system-level interactions between CPS and humans relevant for the societal acceptance of such systems. To reach this quality gate, the expressivity of the model must go beyond classical viewpoints such as operational, functional, and architectural views and views used for safety and security analysis. The model does so by incorporating elements of such systems for mutual introspections in situational awareness, capabilities, and intentions to enable a synergetic, trusted relation in the interaction of humans and CPSs, which we see as a prerequisite for their societal acceptance. The reference architecture is represented as a metamodel incorporating conceptual and behavioral semantic aspects. We illustrate the key concepts of the metamodel with examples from cooperative autonomous driving, the operating room of the future, cockpit-tower interaction, and crisis management.

Continuous integration (CI) pipelines are commonly used to execute regression tests before pull requests are merged. Regression test selection (RTS) aims to reduce the required testing effort and feedback time for developers. However, existing RTS techniques are imprecise for tests with cross-language links to compiled C++ binaries or unsafe if tests use external files. This is problematic because modern software in fact involves several programming languages and (non-)code artifacts such as configuration files. In this paper, we present BinaryRTS, a novel RTS technique that leverages dynamic binary instrumentation to collect the covered functions and accessed external files for each test. BinaryRTS then selects tests depending on changes issued to C++ binaries or external (non-)code artifacts. When evaluating BinaryRTS in our large-scale industrial context, we are able to exclude on average up to 74% of tests without missing real failures. We release BinaryRTS as the first publicly available RTS tool for software involving C++ code.

Fuzzing is a widely used automated testing technique that uses random inputs to provoke program crashes indicating security breaches. A difficult but important question is when to stop a fuzzing campaign. Usually, a campaign is terminated when the number of crashes and/or covered code elements has not increased over a certain period of time. To avoid premature termination when a ramp-up time is needed before vulnerabilities are reached, code coverage is often preferred over crash count to decide when to terminate a campaign. However, a campaign might only increase the coverage on non-security-critical code or repeatedly trigger the same crashes. For these reasons, both code coverage and crash count tend to overestimate the fuzzing effectiveness, unnecessarily increasing the duration and thus the cost of the testing process.

The rapid deployment of digital systems into all aspects of daily life requires embedding social constructs into the digital world. Because of the complexity of these systems, there is a need for technical support to understand their actions. Social concepts, such as explainability, accountability, and responsibility rely on a notion of actual causality. Encapsulated in the Halpern and Pearl's (HP) definition, actual causality conveniently integrates into the socio-technical world if operationalized in concrete applications. To the best of our knowledge, theories of actual causality such as the HP definition are either applied in correspondence with domain-specific concepts (e.g., a lineage of a database query) or demonstrated using straightforward philosophical examples. On the other hand, there is a lack of explicit automated actual causality theories and operationalizations for helping understand the actions of systems. Therefore, this paper proposes a unifying framework and an interactive platform (Actual Causality Canvas) to address the problem of operationalizing actual causality for different domains and purposes. We apply this framework in such areas as aircraft accidents, unmanned aerial vehicles, and artificial intelligence (AI) systems for purposes of forensic investigation, fault diagnosis, and explainable AI. We show that with minimal effort, using our general-purpose interactive platform, actual causality reasoning can be integrated into these domains.

Accountability is the property of a system that enables the uncovering of causes for events and helps understand who or what is responsible for these events. Definitions and interpretations of accountability differ; however, they are typically expressed in natural language that obscures design decisions and the impact on the overall system. This paper presents a formal model to express the accountability properties of cyber-physical systems. To illustrate the usefulness of our approach, we demonstrate how three different interpretations of accountability can be expressed using the proposed model and describe the implementation implications through a case study. This formal model can be used to highlight context specific-elements of accountability mechanisms, define their capabilities, and express different notions of accountability. In addition, it makes design decisions explicit and facilitates discussion, analysis and comparison of different approaches.

While the topic of security in industrial applications has gained some momentum in recent years, there are still severe security vulnerabilities which are actively exploited for attacks. The robot operating system (ROS) is expected to further grow in usage and to be used in many industrial applications. Analysis, however, shows that it lacks several security enhancements in order to make it suitable for industrial use. In its current state, false data and commands can be injected posing a possible safety risk for the resulting product and humans in the production. In addition, data may be eavesdropped and used by outsiders to gain insight into the production process. In this paper we propose a security architecture intended for use on top of ROS on the application level. We use a dedicated authorization server to ensure that only valid nodes are part of the application. Cryptographic methods ensure data confidentiality and integrity. We show in a demonstration with a collaborative robot how our architecture can be used to secure a ROS-based application.