Software-as-a-Service solutions are increasingly being adopted when developing software applications, as they are scalable, cost-effective, and facilitate rapid deployment while providing high availability and flexibility. However, the impact of Software-as-a-Service in terms of carbon emissions is not yet adequately addressed as a design concern, and most of the existing efforts revolve around measuring and containing the carbon impact after the deployment. Our work proposes a model-driven reasoning framework that integrates UML-based software architecture modeling with carbon-aware concerns. Architectural elements are supplemented with sustainability and performance properties of interest through a dedicated Domain Specific Language; then, a model-driven transformation generates a simulation model to evaluate multiple architectural designs according to their Software Carbon Intensity and performance metrics. The results guide decision-making by assessing and comparing the trade-offs between performance and carbon intensity for the analyzed designs. In this way, the reasoning framework provides an automated, tool-supported approach to designing environmentally responsible Software-as-a-Service applications.

Cyber-physical systems (CPS) are challenging to control due to the complex uncertainties arising from physical and virtual sources. Enhancing CPS with self-adaptation is beneficial in addressing these uncertainties. While reactive adaptation often struggles with reliability, proactive adaptation could be more advantageous by preparing systems to make informed decisions, considering the consequences of changes before they occur. CPS and their execution environment usually exhibit timevarying or non-linear dynamics, which are more complex to predict than linear systems, while recent proposals of proactive self-adaptation methods have focused on linear systems. This work bridges this gap by proposing a method for Proactive self-Adaptation for Nonlinear Cyber-physical Systems (PANCS). PANCS is developed through a ground vehicle running example, leveraging MAPE-K loop, and its strengths and limitations are discussed.

Business process management deals with the administration of the chains of events, activities, and decisions that add value to an organization. The ever-growing complexity and inner uncertainty of business-related operations call for a paradigm shift toward new technologies, such as digital twins, to coordinate and optimize the processes belonging to an organization. In this paper, we propose an extended version ofthe business process lifecycle that includes a Digital Twin of the Organization, in charge of consistently ensuring the process’s alignment with the required performance goals and facilitating recovery from failures. We also contribute to defining the model-driven components that transform the business process model, in BPMN notation, into a compatible process representation for the digital twin, enabling simulation capabilities and scenario analyses. Through a manufacturing use case, we demonstrate how the digital twin optimizes processes pre-implementation and facilitates rapid recovery and reconfiguration following critica lfailures, underscoring its potential to improve efficiency and reliability in process management.

As simulation applications become vital for understanding and predicting complex systems, analyzing data from repeated simulation runs is essential to gauge model uncertainty and identify optimal parameter settings. This paper presents a visualization tool for analyzing time series ensemble data generated by discrete-event simulations, focusing on feature importance within clustering results. The tool combines dimensionality reduction, clustering, and SHapley Additive exPlanations (SHAP) to highlight influential features and identify trends within clustered simulation data, advancing previous approaches focusing solely on visualization or clustering without analyzing specific feature contributions. By analyzing a manufacturing use case, we show how the visualization supports decision-makers by depicting the main features driving cluster formation and displaying time intervals critical to characterizing distinct system behaviors.

Digital Twin of the Organization (DTO) is a relatively new concept that emerged to help managers have a full understanding of their organization and realize their objectives. Indeed, DTO enables connecting all the elements of an organization virtually by providing monitoring, optimization, prediction, and other capabilities through continuous simulations. Creating a flexible and evolvable DTO that covers and supports the organization's business strategies is a complex and time-consuming task that requires engineering best practices. In this context, this paper presents and evaluates the EA Blueprint Pattern, which serves as an architectural reference for the development of a DTO by allowing for mapping well-known Enterprise Architecture concepts into software components defining the DTO software architecture. The evaluation is carried on by showing how to use the pattern for creating the DTO for a given organization. Then, a thorough discussion is conducted to analyze how the developed DTO should evolve to deal with vertical and horizontal integration. The lessons learned highlight the strengths and weaknesses along with practical implications for organizations that are eager to develop their DTO according to the EA Blueprint Pattern.

Digitization of the manufacturing and assembly sector is important to set up Industry 4.0. In this process, one of the key factors is the channel of sharing and distributing information on the shop floor. This study highlights the implementation of digital work instructions in the manufacturing and assembly sectors and finds the benefits that it could bring to the industry. The study was conducted in a large production plant with over five hundred workers in Malaysia and was carried out for almost a year. Whereas, most of the existing studies have been conducted in a controlled environment with a group of inexperienced workers in manufacturing and assembly tasks. In this article, the benefits and challenges of digital work instructions are studied over paper-based textual representation of assembly instructions. The study was conducted among groups of people with different roles, such as electrical assembly, mechanical assembly, and final quality check. The qualitative analysis is carried out based on the survey conducted among operators with different roles. Results show that digitalization eases the work for the quality inspection group. In contrast, people with other tasks are either neutral or find it more difficult to work with digitalized versions over paper-based instructions. In addition to this, some data-driven facts are presented, which help in improving the plant operations. This includes recording material shortages, optimizing working hours, and having real-time updates on production status which leads to effective production planning. At last, with the collected information, manufacturing plants can also optimize power utilization that impacts the environment in a positive direction.

Architecture refactoring is a big challenge and requires thorough analysis and labor-intensive, error-prone activities to restructure functionalities from a legacy architecture to a new intended one. Indeed, source code should be adapted to match the new structure. In this context, automatically mapping source code to the intended architecture would significantly reduce manual work and prevent technical debt. To this end, in this paper, we aim to map methods to architectural modules solely defined by textual descriptions, i.e., formulated as a machine learning text classification problem. Methods are mapped into modules using different approaches. We apply the proposed approach to an open-source software system, results show that vectorizing text and code using large language models outperforms other modern methods. The different applied machine learning classifiers perform comparably well, where the best attain accuracy of around 40% and F1-score of around 30%.

Microservice architectures have gained enormous popularity due to their ability to be dynamically added/removed, replicated, and updated according to run-time needs. However, the dynamic nature of microservices introduces uncertainty, which in turn can affect the provided Quality of Service (QoS). This calls for novel service discovery mechanisms able to adapt to the variability of the QoS attributes and further perform effective service discovery and selection. To this end, this paper combines machine learning and self-adaptation techniques to perform service discovery and selection by trading off different QoS attributes. The results of our validation on a state-of-the-art microservices exemplar show that our ML-enabled approach can perform service discovery with 35% higher effectiveness with respect to existing baselines.

The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

Robots are increasingly adopted in a wide range of unstructured and uncertain environments, where they are expected to keep quality properties such as efficiency, accuracy, and safety. To this end, robots need to be smart and continuously update their situation awareness. Self-adaptive systems pave the way for accomplishing this aim by enabling a robot to understand its surroundings and adapt to various scenarios in a systematic manner. However, some situations, e.g., adjusting adaptation rules, refining run-time models, narrowing a vast adaptation domain, and taking future scenarios into consideration, etc. may require the self-adaptive system to include additional specialized components. In this regard, this work proposes a novel approach combining the MAPE-K, adaptive controllers, and a Digital Twin of the robot to enable the managing system to be aware of new scenarios appearing at run-time and operate safely, accurately, and efficiently. A state-of-the-art robot model is employed to evaluate the suitability of the approach.