• 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.

Determining which Large Language Model (LLM) is superior for code smell detection is a complex challenge. This study aims to establish a systematic methodology and evaluation matrix to address this question. We introduce a curated dataset containing smelly code implementations of identical scenarios across four major programming languages: Java, Python, JavaScript, and C++. Each dataset entry is annotated with known code smells, serving as ground truth for evaluation. We benchmark two state-of-the-art LLMs—DeepSeek-V3 and OpenAI GPT-4.0—analyzing their detection capabilities using precision, recall, and F1-score. Our evaluation spans three levels of granularity: overall model performance, performance per code smell category, and detailed performance per individual code smell type. Additionally, we assess the cost-effectiveness of each model by considering their differing detection techniques—token-based analysis in OpenAI GPT-4.0 versus pattern matching in DeepSeek-V3. The findings provide valuable insights for practitioners aiming to select the most efficient and cost-effective LLM for automated code smell detection.

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.

Close Human-Robot Interaction (HRI) has been a great focus of research for the last decades. The outcomes of this focus is a new field in industrial robotics called collaborative robotics. A collaborative robot (cobot) is usually an industrial robot designed to operate safely in a shared work environment with the human worker. This in contrast to conventional Industrial Robots (IRs) which are operating in isolation from the worker workspace, the cobot is changing the concept of automation from fully automated operations to semiautonomous operations, where the decisions of the worker will influence the actions of the cobot and viceversa. Therefore, a communication and information control framework must exist to connect the worker and the cobot together to fulfil this semi-autonomous paradigm. This framework should be able to provide a method to represent the common knowledge which can support the collaborative manufacturing between the worker and the cobot. During this research we are proposing an ontology-based Holonic Control Architecture (HCA) as a proper solution to share and communicate the knowledge needed to achieve complex interaction scenarios between the worker and the cobot.

Involving an industrial robot in a close physical interaction with the worker became quite possible, as a result of the availability of different collaborative industrial robots in the market. The physical cooperation between the industrial robot and the worker usually done under the umbrella of the flexible manufacturing paradigm, where both the industrial robot and the worker need to change their tasks fast and efficiently, to cope with the changes in the manufacturing process. This means that a reliable manufacturing control system must stand behind this physical interaction to achieve the proper communication interaction. A holonic control architecture is an ideal solution for this problem. Therefore, during this research we study the most commonly applied model of the holonic control architecture, then we apply this architecture on our case study, where one worker cooperates with a dual-arm industrial robot to build and produce any new product. Also the research uses the worker's hand gesture recognition as a method to interact with the industrial robot during the execution of a cooperative production scenario.

S.130-139