As automated systems advance in complexity, comprehensive testing becomes crucial, particularly for human–robot interaction (HRI) environments where human unpredictability creates ill-defined testing domains that challenge conventional software testing approaches. In interactive robotics, evaluation criteria extend beyond performance to include critical safety considerations. This paper introduces a novel automated testing framework combining runtime monitoring with constraint-based techniques for HRI environments. The framework employs a three-level cognitive oracle architecture – observation, interpretation, and diagnosis – that automatically evaluates the correctness of human and robot actions without requiring expert human intervention. The approach uses constraint-based modeling to handle the non-deterministic nature of HRI scenarios while ensuring safety compliance. Validation through five test cases in a refrigerator disassembly simulation demonstrates the framework’s effectiveness in detecting safety violations and procedural errors under environmental uncertainties.

Learning to perform procedural motion or manipulation tasks in unstructured or uncertain environments poses significant challenges for intelligent agents. Although reinforcement learning algorithms have demonstrated positive results on simple tasks, the hard-to-engineer reward functions and the impractical amount of trial-and-error iterations these agents require in long-experience streams still present challenges for deployment in industrially relevant environments. In this regard, interactive reinforcement learning has emerged as a promising approach to mitigate these limitations, whereby a human supervisor provides evaluative or corrective feedback to the learning agent during training. However, the requirement of a human-in-the-loop approach throughout the learning process can be impractical for tasks that span several hours. This study aims to overcome this limitation by automating the learning process and substituting human feedback with an artificial supervisor grounded in constraint-based modeling techniques. In contrast to the logical constraints commonly used for conventional reinforcement learning, constraint-based modeling techniques offer enhanced adaptability in terms of conceptualizing and modeling the human knowledge of a task. This modeling capability allows an automated supervisor to acquire a closer approximation to human reasoning by dividing complex tasks into more manageable components and identifying the associated subtask and contextual cues in which the agent is involved. The supervisor then adjusts the evaluative and corrective feedback to suit the specific subtask under consideration. The framework was assessed using three actor-critic agents in a human–robot interaction environment, demonstrating a sample efficiency improvement of 50% and success rates of ≥ 95% in simulation and 90% in real-world implementation.

Abstract In the last years, the use of microservice architectures is spreading in Cyber-Physical Systems (CPSs) and Internet of Things (IoT) domains. CPSs are systems that integrate digital cyber computations with physical processes. The development of software for CPSs demands a constant maintenance to support new requirements, bug fixes, and deal with hardware obsolescence. The key in this process is code testing and more if the code is fragmented during the development of CPSs. It is important to remark that this process is challenging and time-consuming. In this paper, we report on the experience of instantiating of the microservice-based architecture for DevOps of CPSs to test elevator dispatching algorithms across different test levels (i.e., SiL, HiL and Operation). Such an architecture allows for a continuous deployment, monitoring and validation of CPSs. By integrating the approach with a real industrial case study, we demonstrate that our approach reduces significantly the time needed in the testing process and consequently, reduces the economic cost of the entire process.

While most large web-based software systems (e.g., Amazon, Google) release a new software version every almost a minute, in the context of Cyber-Physical Systems (CPSs), this is still far. However, the software of CPSs needs to evolve while these are in operation to fix bugs, add new functionalities, carry out refactoring activities and deal with unforeseen situations that were discovered while the CPS was operating. In the last three years, the Adeptness project has been developing in a solution to help speedup the software release of CPSs that are in operation while guaranteeing their reliability. In this paper, we summarize the objectives, results and lessons learned from this H2020 project.

Abstract The software of systems of elevators needs constant maintenance to deal with new functionality, bug fixes, or legislation changes. To automatically validate the software of these systems, a typical approach in industry is to use regression oracles, which execute test inputs both in the software version under test and in a previous software version. However, these practices require a long test execution time and cannot be re‐used at different test phases. To deal with these issues, we propose Dispatching AlgoRIthm Oracle (DARIO), a test oracle that relies on regression machine‐learning algorithms to detect both functional and non‐functional problems of the system. The machine‐learning algorithms of this oracle are trained by using data from previously tested versions to predict reference functional and non‐functional performance values of the new versions. An empirical evaluation with an industrial case study demonstrates the feasibility of using our approach. A total of five regression learning algorithms were validated by using mutation testing techniques. For the context of functional bugs, the accuracy when predicting verdicts by DARIO ranged between 95% and 98%, across the different scenarios proposed. For the context of non‐functional bugs, were competitive too, having an accuracy when predicting verdicts by DARIO ranged between 83% and 87%.

The software of elevators requires maintenance over several years to deal with new functionality, correction of bugs or legislation changes. To automatically validate this software, test oracles are necessary. A typical approach in industry is to use regression oracles. These oracles have to execute the test input both, in the software version under test and in a previous software version. This practice has several issues when using simulation to test elevators dispatching algorithms at system level. These issues include a long test execution time and the impossibility of re-using test oracles both at different test levels and in operation. To deal with these issues, we propose DARIO, a test oracle that relies on regression learning algorithms to predict the Qualify of Service of the system. The regression learning algorithms of this oracle are trained by using data from previously tested versions. An empirical evaluation with an industrial case study demonstrates the feasibility of using our approach in practice. A total of five regression learning algorithms were validated, showing that the regression tree algorithm performed best. For the regression tree algorithm, the accuracy when predicting verdicts by DARIO ranged between 79 to 87%.

This work presents a critical analysis of the suitability of surrogate models for finite element method application. A case study of a finite element method (FEM) structural problem was selected in order to test the performance of surrogate algorithms. A simple design of experiments (DoE) approach, based on 1D kernel density estimations, is employed to construct a representative pool of real FEM simulations, which becomes the dataset for five different surrogate models, two linear and three non-linear, whose most relevant hyperparameters were tuned (model selection). Results in a real bushing case study show that surrogate models can accurately mimic FEM simulations outcomes, in this case four types of stiffnesses (axial, radial, torsion, and cardanic).

In an automated industrial environment, a large volume of data and signals is available, both from sensors and actuators in machinery and from the interaction with operators and users. Operation diagnosis can have multiple applications from a learning point of view (e.g. staff training) or in terms of process assessment. This work proposes a methodology for implementing an Intelligent System by means of any interactive system connected through OPC UA standard. A digital twin of the process supports configuration and validation, prior to commissioning. Activity is interpreted and diagnosed according to the context in which it occurs. Step order in sequence, step duration and sequence duration are analyzed in a use case based on a PLC-controlled robotic cell in which it is operated both in automatic and manual mode for adjusting a linear table's positioning.

Software systems that are embedded in autonomous Cyber-Physical Systems (CPSs) usually have a large life-cycle, both during its development and in maintenance. This software evolves during its life-cycle in order to incorporate new requirements, bug fixes, and to deal with hardware obsolescence. The current process for developing and maintaining this software is very fragmented, which makes developing new software versions and deploying them in the CPSs extremely expensive. In other domains, such as web engineering, the phases of development and operation are tightly connected, making it possible to easily perform software updates of the system, and to obtain operational data that can be analyzed by engineers at development time. However, in spite of the rise of new communication technologies (e.g., 5G) providing an opportunity to acquire Design-Operation Continuum Engineering methods in the context of CPSs, there are still many complex issues that need to be addressed, such as the ones related with hardware-software co-design. Therefore, the process of Design-Operation Continuum Engineering for CPSs requires substantial changes with respect to the current fragmented software development process. In this paper, we build a taxonomy for Design-Operation Continuum Engineering of CPSs based on case studies from two different industrial domains involving CPSs (elevation and railway). This taxonomy is later used to elicit requirements from these two case studies in order to present a blueprint on adopting Design-Operation Continuum Engineering in any organization developing CPSs.

Cloud computing technologies such as virtualization and orchestration have revolutionized a great number of different domains, from the industry to healthcare. However, there are still certain applications that can not take advantage of this paradigm due to stricter requirements such as end-to-end response times. A new paradigm called Fog computing is arising to answer the needs of these applications. Fog nodes have lower hardware resources that their Cloud counterparts and are distributed over a wider geographical area. Porting technologies designed for the Cloud, specifically orchestration, to this new paradigm requires to handle these peculiarities. A novel orchestration architecture is proposed evolving on current ones, directly focusing Fog characteristics. Multiple schedulers are dynamically deployed in order to achieve a better scheduling throughput and spread the resources required by the scheduler among multiple lower-resource nodes. It uses an application-centric approach at the scheduler level in order to enable new scheduling parameters. The scheduling throughput of this architecture is compared to the most common Cloud orchestration architecture.

Current Cyber-Physical Systems (CPS) must cope with complex requirements that involve diverse hardware and software platforms, communication networks and middleware, legacy systems integration, programming languages, or tools and methodologies. Thus, one of the most challenging aspects in such systems is heterogeneity. On the other hand, such systems often integrate resource constrained embedded systems, and therefore they must optimize the available resources to meet specific constraints in terms of e.g. CPU usage or energy consumption. In this context, SCA (Service Component Architecture) arises as a promising technology to deal with heterogeneity, as far as it provides a broad range of features that make it suitable for integrating heterogeneous software components in a distributed application. Nevertheless, it does not provide specific support for distributed resource management. This paper presents a component platform that provides basic resource management and provisioning for SCA based distributed applications, using well known real time mechanisms provided by the operating system underneath.

Component Based Software Engineering (CBSE) is being increasingly applied in the distributed embedded systems (DES) domain as long as these systems are getting more and more complex in terms of flexibility, dynamism or heterogeneity. Besides that, safety critical systems must cope with the fulfillment of safety requirements and certification standards. This factor increases considerably the development cost of safety distributed embedded systems, even more if they must cope with flexibility, dynamism and heterogeneity. This paper focuses on the distribution aspects of such systems, and more specifically on safe communication channels for safety critical distributed systems. The proposed approach describes a certifiable general purpose safety communication layer that could be reused in different systems, thereby reducing the cost of system development and certification.

The increasing complexity in current distributed embedded systems (DES) makes its design a hard task that involves several aspects like distribution middleware selection, QoS support, implementation language selection, application dynamic reconfiguration or application deployment and maintenance. These design considerations must provide a solution to non functional requirements that distributed applications nowadays demand (e.g. Hardware heterogeneity, real time requirements, energy constraints, security, safety or fault tolerance). This paper proposes a management platform for distributed component based applications that follow the SCA (Service Component Architecture) standard. This platform provides several functionalities that range from application deployment and life cycle control to QoS and fault tolerance support.

Industrialized societies are increasingly dependent on distributed real time systems (DRTS) that are getting more and more complex, dynamic, open, while interacting with a progressively more demanding and heterogeneous environment. In such an scenario, middleware technologies that provide real-time or QoS support are gaining importance, as they ease the development and deployment of complex distributed applications in heterogeneous networks while ensuring certain time and QoS constraints. In this sense, distribution middleware technologies like Data Distribution Service (DDS) have emerged, adding extensive QoS support to traditional middleware advantages. This paper proposes an additional middleware layer on top of DDS, based on components, that provides an easy way to deploy, configure and manage component based distributed applications, considering fault tolerance aspects.

Service Oriented architectures are becoming very popular to meet the requirements of complex current applications demanding distribution and heterogeneity in open environments. In this sense, the iLAND-ARTEMIS project is aimed at the development of a service-based middleware framework capable of supporting deterministic dynamic functional composition and reconfiguration of networked embedded service oriented applications. The approach proposed in this paper uses Model Driven Engineering (MDE) in order to guide the development of the iLAND applications from the specification to the execution through code generation, focusing on reconfiguration behavior at run-time, and assuring that non-functional requirements are met by an adequate resource assignment.

Industrialised societies are increasingly dependent on embedded systems that are getting more and more complex, dynamic, open, while interacting with a progressively more demanding and heterogeneous environment.