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.

Self-adaptation equips a software system with a feedback loop that resolves uncertainties during operation and adapts the system to deal with them when necessary. Most self-adaptation approaches today use decision-making mechanisms that select for execution the adaptation option with the best-estimated benefit expressed as a set of adaptation goals. A few approaches also consider the estimated (one-off) cost of executing the candidate adaptation options. We argue that besides benefit and cost, decision-making in self-adaptive systems should also consider the estimated risk the system or its users would be exposed to if an adaptation option were selected for execution. Balancing all three concerns when evaluating the options for adaptation to mitigate uncertainty is essential for satisfying stakeholders’ concerns and ensuring the safety and public acceptance of self-adaptive systems. In this paper, we present a reference model for decision-making in self-adaptation that considers the estimated benefit, cost, and risk as core concerns of each adaptation option. Leveraging this model, we then present an ISO/IEC/IEEE 42010 compatible architectural viewpoint that aims at supporting software architects responsible for designing robust decision-making mechanisms for self-adaptive systems. We demonstrate the applicability, usefulness, and understandability of the viewpoint through a case study where participants with experience in the engineering of self-adaptive systems performed a set of design tasks in DeltaIoT, an Internet-of-Things exemplar for research on self-adaptive systems.

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.

A relevant issue in managing a complex software system is the quantitative assessment of the fulfillment degree of its requirements, as it provides a sound basis for identifying suitable design time or runtime corrective actions. However, this quantitative assessment must cope with the uncertainty that could affect the requirements specification and the data used in the assessment process. We tackle this problem, assuming the system requirements are specified through a hierarchical goal model. For this case, we propose an approach rooted in the fuzzy set and fuzzy logic theory for the quantitative assessment of the fulfillment degree of the goals specified in the goal model, considering the different kinds of uncertainty that could be present. Our result enriches the methodological and practical toolset available for managing complex software systems in uncertain scenarios, complementing other approaches based on different formal frameworks like probability theory.

Pixel-wise time-series analysis of satellite-derived vegetation indices is often complex and computationally demanding, as the pixel-wise application of the analysis scales with the number of pixels to be processed. In this study, we use Gaussian Process Regression (GPR) pixel-wise to fill temporal gaps in Normalized Difference Vegetation Index (NDVI) data. Rather than applying GPR across all pixels and trying to optimise the performance of GPR directly, we introduce an algorithm to select key pixels for GPR applications. By selectively applying GPR to a subset of pixels and image reconstruction of the remaining unselected pixels, we achieve substantial speedups (2.5-5.5×) with minimal loss in NDVI estimation accuracy (0.5-2.5%). This trade-off with pixel selection enables more scalable and efficient pixel-wise processing of large-scale imagery, with strong applicability in the field of remote sensing.

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.

Antifragility is one of the terms that have recently emerged with the aim of indicating a direction that should be pursued toward the objective of designing Information and Communications Technology systems that remain trustworthy despite their dynamic and evolving operating context. We present a characterization of antifragility, aiming to clarify from a conceptual viewpoint the implications of its adoption as a design guideline and its relationships with other approaches sharing a similar objective. To this end, we discuss the inclusion of antifragility (and related concepts) within the well-known dependability taxonomy, which was proposed a few decades ago with the goal of providing a reference framework to reason about the different facets of the general concern of designing dependable systems. From our conceptual characterization, we then derive a possible path toward the engineering of antifragile systems.

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.

Sources of uncertainty in adaptive systems are rarely independent, and their interaction can affect the attainment of system goals in unpredictable ways. Despite ample work on “taming” uncertainty, the research community has devoted little attention to the systematic representation, analysis, and mitigation of uncertainty propagation and interaction (UPI) in adaptive systems. To address this oversight, we introduce Uncertainty Flow Diagrams, a notation that captures key UPI aspects. We demonstrate the use and benefits of our novel notation on Znn.com, an adaptive news site infrastructure.