• Reviews runtime composition in the dynamic System of Systems (SoS) • Identifies key challenges: modeling, orchestration, resilience, heterogeneity • Synthesizes seven solution strategies from recent SoS literature • Maps tools and evaluation methods used in runtime SoS research • Reveals gaps in integration, benchmarking, and socio-technical alignment Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition —the on-the-fly discovery, integration, and coordination of constituent systems (CSs)—is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composition in dynamic SoSs. This study synthesizes research on runtime composition in dynamic SoSs and identifies core challenges, solution strategies, supporting tools, and evaluation methods. We conducted a Systematic Literature Review (SLR), screening 1,774 studies published between 2019 and 2024 and selecting 80 primary studies for thematic analysis (TA). Challenges fall into four categories: modeling and analysis, resilient operations, system orchestration, and heterogeneity of CSs. Solutions span seven areas: co-simulation and digital twins, semantic ontologies, integration frameworks, adaptive architectures, middleware, formal methods, and AI-driven resilience. Service-oriented frameworks for composition and integration dominate tooling, while simulation platforms support evaluation. Interoperability across tools, limited cross-toolchain workflows, and the absence of standardized benchmarks remain key gaps. Evaluation approaches include simulation-based, implementation-driven, and human-centered studies, which have been applied in domains such as smart cities, healthcare, defense, and industrial automation. The synthesis reveals tensions, including autonomy versus coordination, the modeling-reality gap, and socio-technical integration. It calls for standardized evaluation metrics, scalable decentralized architectures, and cross-domain frameworks. The analysis aims to guide researchers and practitioners in developing and implementing dynamically composable SoSs.

Data and databases are the most important assets for enterprises today, driving informed decision-making, enhancing operational efficiency, serving as valuable intangible assets, enabling digital transformation, and mitigating risks through proper management and security. Organizations are working on solutions to ensure data security, integrity, and availability while enabling smooth data interoperability and convergence. However, data security of sensitive information remains a big challenge, particularly in the healthcare sector. Built on basic data security principles like hashes and encryption, blockchain is a distributed system that keeps identical copies across multiple nodes, therefore ensuring data consistency and integrity. In this paper, we explored the potential of integrating blockchain technology with contemporary relational database systems. We used Python and SQL to simulate and analyze the compatibility between blockchain data formats and regular relational databases. Using experimental scenarios within a RDBMS environments, we explored how blockchain's immutable and decentralized record system might increase data security and integrity when integrated with conventional database systems. This paper investigates the possibilities of combining blockchain's unchangeable and distributed document-retaining mechanism with the well-established structure of relational databases with an eye toward how blockchain and relational databases might be used together to improve the security and alignment of healthcare data. The aim of this paper is to discuss the issues in existing systems and to characterize both blockchain and relational database technologies. We explore blockchain data system-oriented techniques and scripting in the RDBMS environment to develop practical approaches to enhance data security and interoperability in healthcare systems.

In software engineering, the integration of machine learning (ML) and artificial intelligence (AI) components into modern web services has become commonplace. To comply with evolving regulations, such as the EU AI Act, the development of AI models must adhere to the principles of transparency. This includes the training data used, the intended use, potential biases, and the risks associated with these models. To support these goals, documents named Model Cards were introduced to standardize ethical reporting and allow stakeholders to evaluate models based on various goals. In our ongoing research, we aim to automate risk analysis and regulatory compliance checks in software systems. We envision that model cards can serve as useful tools to achieve the goal. Given the evolving format of model cards over time, we conducted a state-of-the-art review of the current state and practice of model cards by analyzing 90 model cards from four model repositories to assess their relevance to our vision. The study's contribution is a thorough analysis of the model cards' structure and content, as well as their ethical reporting. Our study reveals the variance in information reporting, the loose structure, and the lack of ethical reporting in the model cards. Based on the findings, we propose a unified model card template that aims to enhance the structure, promote greater transparency, and establish a foundation for future machine-interpretable AI model cards.

Abstract Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This position paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team’s work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

There is an increasing need for practitioners to address legislative and ethical issues in both the development and deployment of data-driven applications with AI/ML due to growing concerns and regulations, such as GDPR and the EU AI Act. Thus, the field needs a systematic framework for assessing risks and helping to stay compliant with regulations in designing and deploying software systems. Clear and concise descriptions of risks associated with each model and data source are needed to guide the design without acquiring deep knowledge of the regulations. In this paper, we propose a reference architecture for an ethical orchestration system that manages distributed AI/ML applications on the cloud–edge continuum and present a proof-of-concept implementation of the main ideas of the architecture. Our starting point is the methods already in use in the industry, such as model cards, and we extend the idea of model cards to data source cards and software component cards, which provide practitioners and the automated system with relevant information in actionable form. With the metadata card based orchestration system and information about the risk levels of the target infrastructure, the users can create deployments of distributed AI/ML systems that fulfill the regulatory and other requirements.

In the era of 6G, developing and managing software requires cutting-edge software engineering (SE) theories and practices tailored for such complexity across a vast number of connected edge devices. Our project aims to lead the development of sustainable methods and energy-efficient orchestration models specifically for edge environments, enhancing architectural support driven by AI for contemporary edge-to-cloud continuum computing. This initiative seeks to position Finland at the forefront of the 6G landscape, focusing on sophisticated edge orchestration and robust software architectures to optimize the performance and scalability of edge networks. Collaborating with leading Finnish universities and companies, the project emphasizes deep industry-academia collaboration and international expertise to address critical challenges in edge orchestration and software architecture, aiming to drive significant advancements in software productivity and market impact.

The ever-growing complexity and dynamic nature of modern System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems (CS), referred to as holons in the holonic architecture of SoS. This paper proposes a novel approach to enhance humand-to-holon and holon-to-holon communication within the holonic architecture through the integration of Natural Language Processing (NLP) techniques. Our proposed framework utilizes advancements in NLP, specifically Large Language Models (LLMs), enabling holons to understand and act on natural language instructions. This enables more intuitive holon-to-holon and human-to-holon interactions, leading to better coordination among diverse systems. The framework’s practical application is demonstrated through an Unmanned Vehicle Fleet (UVF) case study, showcasing its potential in enhancing communication and coordination in complex SoS. Additionally, we propose evaluat ion strategies to assess the efficiency and effectiveness of this framework, and identify areas for improvement. This work sets the stage for future exploration and prototype implementation, paving the way for further advancements in SoS communication and collaboration.

Recent years have seen the rise of a new generation of UI frameworks for web application development. These frameworks differ from previous generations of JavaScript frameworks in that they define a declarative application development model, where transitions in the state of the UI are managed by the framework. This potentially greatly simplifies application development, but requires the framework to implement a rendering strategy which translates changes in application state into changes in the state of the UI. The performance characteristics of these rendering strategies have thus far been poorly studied. In this article, we describe the rendering strategies used in the frameworks Angular, React, Vue, Svelte and Blazor, which represent some of the most influential and widely used modern web frameworks. We find significant differences in the scaling of costs in their rendering strategies with potentially equally significant practical performance implications. To verify these differences, we implement a number of benchmarks that measure the scaling of rendering costs as an application grows in complexity. The results of our benchmarks confirm that under certain circumstances, performance differences between frameworks can range up to several orders of magnitude when performing the same tasks. Furthermore, we find that the relative performance of a rendering strategy can be effectively estimated based on factors affecting the input sizes of render loops. The best performing rendering strategies are found to be ones which minimize input sizes using techniques such as compile-time optimization and reactive programming models.

Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications, as well as for improving old applications, particularly in the domain of smart healthcare. However, utilizing this data, especially when it is continuously generated by sensors and other devices, with the current approaches is complex—data is often using proprietary formats and storage, and mixing and matching data of different origin is not easy. Furthermore, many of the systems are such that they should stimulate interactions with humans, which further complicates the systems. In this article, we introduce the Human Data Model—a new tool and a programming model for programmers and end users with scripting skills that help combine data from various sources, perform computations, and develop and schedule computer-human interactions. Written in JavaScript, the software implementing the model can be run on almost any computer either inside the browser or using Node.js. Its source code can be freely downloaded from GitHub, and the implementation can be used with the existing IoT platforms. As a whole, the work is inspired by several interviews with professionals, and an online survey among healthcare and education professionals, where the results show that the interviewed subjects almost entirely lack ideas on how to benefit the ever-increasing amount of data measured of the humans. We believe that this is because of the missing support for programming models for accessing and handling the data, which can be satisfied with the Human Data Model.

Opportunistic design - an approach in which people develop new software systems by routinely reusing and combining components that were not designed to be used together - has recently become very popular. This emergent pattern places focus on large scale reuse and developer convenience, with the developers "trawling" for most suitable open source components and modules online. The availability of open source assets for almost all imaginable domains has led to software systems in which the visible application code - written by the application developers themselves - forms only the "tip of the iceberg" compared to the reused bulk that remains mostly unknown to the developers. The actual reuse takes place in a rather ad hoc, mix-and-match fashion. In this paper, we take a look at this emerging approach and argue that challenges associated with such development model are quite different from traditional software development.

Today’s dominant design for the Internet of Things (IoT) is a Cloud-based system, where devices transfer their data to a back-end and in return receive instructions on how to act. This view is challenged when delays caused by communication with the back-end become an obstacle for IoT applications with, for example, stringent timing constraints. In contrast, Fog Computing approaches, where devices communicate and orchestrate their operations collectively and closer to the origin of data, lack adequate tools for programming secure interactions between humans and their proximate devices at the network edge. This paper fills the gap by applying Action-Oriented Programming (AcOP) model for this task. While originally the AcOP model was proposed for Cloud-based infrastructures, presently it is re-designed around the notion of coalescence and disintegration, which enable the devices to collectively and autonomously execute their operations in the Fog by serving humans in a peer-to-peer fashion. The Cloud’s role has been minimized—it is being leveraged as a development and deployment platform.

Fog computing paradigm is emerging after a decade's dominance of cloud‐based system design and architecture. Now, instead of centralising the computation and coordination to remote services, these are deployed and distributed to all over physical surroundings and network nodes, including cloud services, smart gateways, and network edge devices. At the moment, the majority of the Internet of things (IoT) systems and software has built on top of open Web‐based technologies. The authors assume that with the ever‐growing number and heterogeneity of connected devices, it becomes ever‐more crucial to have open standards that support interoperability and enable interactions. They review the current technological space for architecting Web technology‐based IoT software in the coming era of fog computing. They focus on fundamental research challenges and discuss the emerging issues.

Nowadays, smart devices have excellent computing power and connectivity and at the same time are used for various purposes. This creates the possibility for complex, cooperative multi-device programs. However, current programming paradigms are not tailored for such a setting. This paper tackles the problem by introducing a new paradigm: an action-oriented programming model. Actions are proactively and pervasively initiated pieces of functionality, which provide synchronized and coordinated joint behavior between several devices. We show how the action-oriented programming model can be realized with a device cloud infrastructure. As a concrete example of an action-oriented infrastructure, we discuss the Social Devices Platform that demonstrates a socio-digital system between devices and people in proximity.

In addition to functions, the architecture of a software system defines numerous other properties, commonly referred to as non-functional or quality properties. In web applications, two well-known architectural styles are common. They are resource-oriented architectural style, implemented using RESTful principles, and message-passing architectural style, implemented in XMPP. These architectural styles have their different benefits, whereas in real applications properties of both are sometimes needed. In this paper we discuss how RESTful web architecture can be complemented with XMPP. As an example, we use a distributed content management system, which is built using RESTful design guidelines and complemented with message passing architectural style implemented on XMPP. Features of both architectural styles are used in a fashion where no negative feature interaction takes place. In the completed system, both architectural styles are clearly identifiable, and their non-functional properties are preserved.

Online social media services, such as Facebook and Twitter, have set new standards on how people interact with each other online, share their everyday activities, and media services. While current mobile services supporting social interaction are typically primarily for remote communication, similar services can be introduced to co-located social interactions. In such a setting, people and proactive, context sensing mobile devices form a new kind of a socio-digital system where the mobile devices are active participants and can initiate interaction among the devices and people. Physical proximity of the devices becomes a key enabler to advance users' interaction with each other and the supporting mobile services. In this paper, we introduce the concept of Social Devices and its implementation. The Social Devices Platform facilitates autonomously composed cooperative services in co-located devices where the client part is simple and easily deployable to different kinds of devices.