Model-driven engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to syntactic constraints hinder the design process. This paper presents a study to evaluate the usefulness of a novel approach utilizing large language models (LLMs) and few-shot prompt learning to assist in domain modeling. The aim of this approach is to overcome the need for extensive training of traditional AI-based completion algorithms on domain-specific datasets and to offer versatile support for various modeling activities, providing valuable recommendations to software modelers. To support this approach, we developed MAGDA, a user-friendly tool, through which we conduct a user study and assess the real-world applicability of our approach in the context of domain modeling, offering valuable insights into its usability and effectiveness.

As insufficient data volume and quality remain the key impediments to the adoption of modern subsymbolic AI, techniques of synthetic data generation are in high demand. Simulation offers an apt, systematic approach to generating diverse synthetic data. This chapter introduces the reader to the key concepts, benefits, and challenges of simulation-based synthetic data generation for AI training purposes, and to a reference framework to describe, design, and analyze digital twin-based AI simulation solutions.

The convergence of modeling simulation (M S) and artificial intelligence (AI) is leaving its marks on advanced digital technology. Pertinent examples are digital twins (DTs) - high-fidelity, live representations of physical assets, and frequent enablers of corporate digital maturation and transformation. Often seen as technological platforms that integrate an array of services, DTs have the potential to bring AI-enabled M S closer to end-users. It is, therefore, paramount to understand the role of M S in DTs, and the role of digital twins in enabling the convergence of AI and M S. To this end, this chapter provides a comprehensive exploration of the complementary relationship between these three. We begin by establishing a foundational understanding of DTs by detailing their key components, architectural layers, and their various roles across business, development, and operations. We then examine the central role of M S in DTs and provide an overview of key modeling techniques from physics-based and discrete-event simulation to hybrid approaches. Subsequently, we investigate the bidirectional role of AI: first, how AI enhances DTs through advanced analytics, predictive capabilities, and autonomous decision-making, and second, how DTs serve as valuable platforms for training, validating, and deploying AI models. The chapter concludes by identifying key challenges and future research directions for creating more integrated and intelligent systems.

Artificial intelligence (AI) and Modeling Simulation (M S) are increasingly intertwined, reflecting converging research needs across both communities, rapid technological advances such as the rise of generative AI, and the growing availability of data and computational resources. This report provides a structured overview of the intersections of AI and M S. The relationship goes both ways: AI can support, augment, or even replace components of simulation studies, while simulations can serve as data generators, training environments, and evaluation platforms for AI. We organize this landscape along the stages of M S from model specification and input modeling to execution, experimentation, verification and validation, and output analysis. Selected studies at each stage illustrates how techniques such as Large Language Models have reshaped simulation practices, while highlighting limitations and open challenges. This report also provides a conceptual roadmap that helps readers navigate a rapidly changing ecosystem.

The surge in reinforcement learning (RL) applications gave rise to diverse supporting technology, such as RL frameworks. However, the architectural patterns of these frameworks are inconsistent across implementations and there exists no reference architecture (RA) to form a common basis of comparison, evaluation, and integration. To address this gap, we propose an RA of RL frameworks. Through a grounded theory approach, we analyze 18 state-of-the-practice RL frameworks and, by that, we identify recurring architectural components and their relationships, and codify them in an RA. To demonstrate our RA, we reconstruct characteristic RL patterns. Finally, we identify architectural trends, e.g., commonly used components, and outline paths to improving RL frameworks.

Studies show that interactions with an AI system fosters trust in human users towards AI. An often overlooked element of such interaction dynamics is the (sense of) urgency when the human user is prompted by an AI agent, e.g., for advice or guidance. In this paper, we show that although the presence of urgency in human-AI interactions does not affect the trust in AI, it may be detrimental to the human user's self-confidence and self-efficacy. In the long run, the loss of confidence may lead to performance loss, suboptimal decisions, human errors, and ultimately, unsustainable AI systems. Our evidence comes from an experiment with 30 human participants. Our results indicate that users may feel more confident in their work when they are eased into the human-AI setup rather than exposed to it without preparation. We elaborate on the implications of this finding for software engineers and decision-makers.

Digital Twins (DT) integrate data from multiple sources. Models consume data and enable DT services such as simulations, what-if analyses, and ML-driven predictions. To ensure proper DT operation, data-driven services require data to exhibit traits such as reliability and high quality (including, e.g., accuracy, completeness, and timeliness). Yet, there is no systematic way to specify data requirements at the model level, and subsequently enact those specifications at runtime. To address this shortcoming, we propose an approach to contract-based quality management in DTs. We formally define a theory of such contracts, situate them architectually within DTs, and propose a domain-specific language to specify contracts. Our approach enables continuous data quality monitoring, thereby improving the reliability and quality of DT services.

This chapter provides an introduction to the foundations of digital twins and makes the case for employing them in smart grids. As engineered systems become more complex and autonomous, digital twin technology gains importance as the unified technological platform for design, testing, operation, and maintenance. Smart grids are prime examples of such complex systems, in which unique design and operation challenges arise from the combination of physical and software components. As high-fidelity in-silico replicas of physical components, digital twins provide safe and cost-efficient experimentation facilities in the design and verification phase of smart grids. In the operation phase of smart grids, digital twins enable automated load balancing of grids through real-time simulation and decision-making. These, and an array of similar benefits, position digital twins as crucial technological components in smart grids.

Virtual training environments are software-intensive systems in which reinforcement learning (RL) agents learn, adapt, and demonstrate meaningful behavior. Virtual training environments offer a safe and cost-efficient alternative to training agents in real-world settings. However, to converge, most realistic RL problems require training in multiple, mostly similar but slightly different environments - i.e., families of environment variants. The typical development process of environment families is a labor-intensive and error-prone manual endeavor that does not scale well. To alleviate these issues, in this paper, we propose a model-driven approach for developing families of RL training environments. To obtain the family of environments, we develop an approach and prototype tool. In our approach, a hybrid genetic algorithm - a combination of population-based global search and heuristic local search - generates environment families. Mutations and constraints are expressed as model transformations and are operationalized into a search process by a state-of-the-art model transformation engine. We demonstrate the soundness of our approach in a wildfire mitigation scenario and curriculum learning - a particular learning paradigm that relies on environment families.

Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.

Model-driven engineering problems often require complex model transformations (MTs), i.e., MTs that are chained in extensive sequences. Pertinent examples of such problems include model synchronization, automated model repair, and design space exploration. Manually developing complex MTs is an error-prone and often infeasible process. Reinforcement learning (RL) is an apt way to alleviate these issues. In RL, an autonomous agent explores the state space through trial and error to identify beneficial sequences of actions, such as MTs. However, RL methods exhibit performance issues in complex problems. In these situations, human guidance can be of high utility. In this paper, we present an approach and technical framework for developing complex MT sequences through RL, guided by potentially uncertain human advice. Our framework allows user-defined MTs to be mapped onto RL primitives, and executes them as RL programs to find optimal MT sequences. Our evaluation shows that human guidance, even if uncertain, substantially improves RL performance, and results in more efficient development of complex MTs. Through a trade-off between the certainty and timeliness of human advice, our method takes a step towards RL-driven human-in-the-loop engineering methods.

Modern systems exhibit unprecedented complexity due to their increased scale, interconnectedness, and the heterogeneity of their digital and physical components. In response to scaling challenges, the system of systems paradigm proposes flexible aggregations of subsystems into a larger whole, while maintaining the independence of subsystems to various degrees. In response to the cyber-physical convergence, the digital twin paradigm proposes a tight coupling between digital and physical components through computational reflection and precise control. As these two paradigms address distinct parts of the overall challenge, combining the two promises more comprehensive methods to engineer what we call systems of twinned systems. The noticeably growing body of knowledge on systems of twinned systems calls for a review of the state of the art. In this work, we report on our systematic literature survey of systems of twinned systems. We screened over 2,500 potential studies, of which we included 80 and investigated them in detail. To converge system of systems and digital twins, we derive a classification framework for systems of twinned systems that is backward compatible with the currently accepted theories of system of systems and digital twins.

Large Language Models (LLMs) offer transformative potential for Modeling Simulation (M S) through natural language interfaces that simplify workflows. However, over-reliance risks compromising quality due to ambiguities, logical shortcuts, and hallucinations. This paper advocates integrating LLMs as middleware or translators between specialized tools to mitigate complexity in M S tasks. Acting as translators, LLMs can enhance interoperability across multi-formalism, multi-semantics, and multi-paradigm systems. We address two key challenges: identifying appropriate languages and tools for modeling and simulation tasks, and developing efficient software architectures that integrate LLMs without performance bottlenecks. To this end, the paper explores LLM-mediated workflows, emphasizes structured tool integration, and recommends Low-Rank Adaptation-based architectures for efficient task-specific adaptations. This approach ensures LLMs complement rather than replace specialized tools, fostering high-quality, reliable M S processes.

Twin transition is the method of parallel digital and sustainability transitions in a mutually supporting way or, in common terms, "greening of and by IT and data." Twin transition reacts to the growing problem of unsustainable digitalization, particularly in the ecological sense. Ignoring this problem will eventually limit the digital adeptness of society and the problem-solving capacity of humankind. Information systems engineering must find ways to support twin transition journeys through its substantial body of knowledge, methods, and techniques. To this end, we systematically survey the academic and gray literature on twin transition, clarify key concepts, and derive leads for researchers and practitioners to steer their innovation efforts.

Collaborative Modeling (CM) tools increasingly offer advanced editing features, such as live co-editing, embedded chat, and automated reasoning. Yet, the impact of these features on the user experience (UX) of modeling is less understood. In this paper, we explore the relationship between CM and UX by developing a mapping between existing CM and UX ontologies. We propose CMUX Explorer, an ontology-driven assessment tool that enables tool builders to identify CM features with UX impact. The conversational front-end allows for natural-language interactions, which CMUX Explorer translates to SPARQL queries, executes them against the ontology, and returns ranked, interpretable recommendations to guide design decisions. Importantly, our approach supports continuous, semi-automated improvement of the ontology, leading to more precise recommendations over time. Through this work, we pave a path towards computer-aided design of UX-aware collaborative modeling tools.

Sustainability is becoming a key property of modern software systems. While there is a substantial and growing body of knowledge on engineering sustainable software, end-to-end frameworks that situate sustainability-related activities within the software delivery lifecycle are missing. In this article, we propose the SusDevOps framework that promotes sustainability to a first principle within a DevOps context. We demonstrate the lifecycle phases and techniques of SusDevOps through the case of a software development startup company.

High autonomy is challenging to achieve in digital twins. This is due to the lack of understanding of the socio-technical challenges and the information needs of digital twin autonomy. In this paper, we contextualize digital twin autonomy in terms of human and technical factors, identify novel socio-technical classes of digital twins with varying levels of autonomy, and define strategies that help improve autonomy across these classes. Our strategies are governed by information valuation models we developed specifically for digital twins. Our approach fosters a systematic top-down technique to improve the autonomy of digital twins.

The widespread adoption of digital twins gave rise to emerging systems of interconnected digital twins, often dubbed aggregated or hierarchical digital twins. In such emerging systems, interoperability of digital twins is key in determining the capabilities and qualitative properties of the emerging system. In this paper, we report on a panel discussion that took place at the 2023 Annual Simulation Conference with four esteemed experts representing four distinct perspectives on the topic: strategic (why aggregated digital twins matter?), technical (how co-simulation supports a distributed set of concerns over multiple digital twins?), standardization (how standards enable interoperability?), and organizational (how organizations deal with digital twinning scenarios?). We report the panelists' main arguments and synthesize them into a discussion. The main takeaway of the panel is that contrary to the state of affairs in digital twinning that limits interoperability to low levels, there is a clear need to reach higher levels of interoperability in digital twinning scenarios that necessitate a distributed approach. Moreover, there are emerging solutions to achieve these higher levels. To provide researchers with tangible leads, we distill challenges and success factors, and recommend future research directions in digital twin interoperability.