Real-time cyber-physical systems in aerospace and other safety-critical domains require deterministic task execution under strict certification constraints. Although static time-triggered scheduling fulfills these requirements, its manual configuration remains error-prone and limits scalability. This article proposes an automated framework that generates optimized schedules for fixed-priority, fully preemptive systems based on the Thales Alenia Space—Italia real-time platform. A simulator that replicates the scheduling semantics of the target platform is implemented, enabling precise analysis and validation of a given schedule plan. The scheduling problem is formulated as a bilevel optimization solved via soft-computing techniques, while a complementary mixed-integer linear programming formulation provides performance bounds. The approach is validated on an industrial satellite case study, demonstrating a significant reduction in preemptions and improved timing consistency compared to manually engineered schedules.

Robotic Process Automation (RPA) uses automation technologies to imitate business tasks performed by humans. Digital Automation Platforms are extremely useful services that enable developers to connect applications and automate work-flows and Workflow Automation Tools (WATs) offer tremendous advantages in orchestrating tasks and services. Even if the workflow composition in WATs is offered in a low-code fashion, the unsupervised and autonomous execution of the tasks and transactions between various software systems are performed even if they are completely unrelated. For this reason, building workflows and optimizing them can be a difficult and time-consuming task. Since the workflow orchestrates external systems, we cannot assure that the execution will not fail, or that unexpected problems will arise during the evolution of the orchestrated systems. In this paper, we investigate these challenges and open issues related to unsupervised workflow declarations, and we envisage a general approach to assist the workflow definition.

Model-Driven Engineering (MDE) is a software engineering methodology focusing on models as primary artifacts. In the last years, the emergence of Web technologies has led to the development of Web-based modeling tools and model-based approaches for the Web that offer a web-based environment to create and edit models or model-based low-code solutions. A common requirement when developing Web-based modeling tools is to provide a fast and efficient way for model management, and this is particularly a hot topic in model-based system engineering. However, the number of approaches offering RESTful services for model management is still limited. Among the alternatives for developing distributed services, there is a growing interest in the use of RESTful services. In this paper, we present an approach to provide RESTful services for model management that can be used to interact with any kind of model, and can be used to build a modeling platform providing modeling-as-a-service. The approach follows the REST principles to provide a stateless and scalable service.

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination is so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of coordination before its execution. However, a formal verification method does not exist for this purpose. In this article, we instead use machine learning to predict the convergence of astrobots swarm. As the first solution to this problem, we propose a weighted$k$-NN-based algorithm that requires the initial status of a swarm and its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms, including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy.Note to Practitioners—Observatories involved in the generation of spectroscopic surveys always encounter limited resources to check the throughputs of their planned observations before their executions. The information yielded by an observation directly depends on the convergence rate of the observatory’s astrobots in that particular observation. Namely, if the astrobots’ convergence rate is below a minimum, then the observation has to be revoked and replanned. Thus, one may define another observation that fulfills the minimum-information requirement. There has been yet no analytical tool developed to verify the convergence rate of the coordination computed by the state-of-the-art trajectory planners of astrobots swarms. Thus, we propose to use a machine learning scheme to predict the desired convergence rate instead of involving in the infeasible process of finding its exact value. This method is a supervised method that requires the target-to-astrobot assignments table of an observation. The algorithm also needs a data set including previous coordination results of various observations of a particular swarm. The simulated scenarios manifest magnificent accuracies in the convergence predictions of some astrobots swarms corresponding to modern spectroscopic surveys, such as SDSS-V (including ~500 astrobots). Our strategy is based on the smallest subset of the astrobots’ features that have a pivotal role in convergence rates, say, the projected positions of targets on a hosting telescope’s focal plane. We argue that more explorations have to be considered to find other important features, such as the motion direction of each astrobot, which may even further improve the obtained prediction accuracies.

Over the last decade, several repositories have been proposed by the Model-Driven Engineering (MDE) community to enable the reuse of modeling artifacts and foster empirical studies to analyze specifications and tools made available by MDE researchers and practitioners. In this respect, different approaches have been proposed to measure the quality of, e.g., models, metamodels, and transformations, with respect to characteristics defined by quality models. However, when a modeling ecosystem is available, measuring the constituting artifacts singularly might not be enough. This paper proposes a quality assessment approach, which considers the relationships among the artifacts under analysis as part of the quality measurement process. For instance, to assess the quality of model transformations, further than measuring their structural characteristics, users might be interested in quality aspects like coverage and information loss related to the depending metamodels and the way models are consumed by transformations, respectively. The proposed approach is based on weaving models, which permit to link quality definitions of different kinds of artifacts, and it can generate Epsilon Object Language (EOL) programs by means of a model-to-code transformation to perform the specified quality assessment process.

Conventional wisdom on model transformations in Model-Driven Engineering (MDE) suggests that they are crucial components in modeling environments to achieve superior automation, whether it be refactoring, simulation, or code generation. While their relevance is well-accepted, model transformations are challenging to design, implement, and verify because of the inherent complexity that they must encode. Thus, defining transformations by chaining existing ones is key to success for enhancing their reusability. This paper proposes an approach, based on well-established algorithms, to support modellers when multiple transformation chains are available to bridge a source metamodel with a target one. The all-important goal of selecting the optimal chain has been based on the quality criteria of coverage and information loss. The feasibility of the approach has been demonstrated by means of experiments operated on chains obtained from transformations borrowed from a publicly available repository.