• 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.

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.

This extended abstract presents an approach to enhance the Fixed-Sized-Candidate-Set Adaptive Random Testing (FSCS-ART) sampling strategy. SWFC-ART, the proposed approach, stores the previously-executed, non-failure-causing test cases into a Hierarchical Navigable Small World Graph (HNSWG) data structure and uses an efficient and consistent Nearest Neighbor Search (NNS) mechanism, especially for high-dimensional input domains. Our experiments show that SWFC-ART reduces the computational overhead of FSCS-ART from quadratic to log-linear order while retaining the failure-detection effectiveness of FSCS-ART.

Although smart speakers are widely accepted by consumers today, especially in developed countries such as the United States (US), few marketing-focused empirical studies on smart speakers have been conducted. To address this gap in the literature, the current study aims to explore the effects of perceived coolness on consumers’ attitudes toward smart speakers through perceived values (i.e., functional, hedonic, economic, and social value). Data were collected from the current smart speaker users in the US using an online questionnaire. The study employed partial least squares structural equation modeling (PLS-SEM) approach on 307 validated responses. The SEM analysis showed that perceived coolness, which consisted of four dimensions: perceived functionality, attractiveness, subcultural appeal, and originality, had a positive effect on the functional, hedonic, economic, and social value. The findings further revealed that consumers’ attitude toward smart speakers was influenced by functional, hedonic, and economic value, but not by social value. Additionally, the attitude was found to be a strong predictor of continuance intention. This study is one of the early attempts to explore the current smart speaker users’ attitudes and their intentions to continue using AI-based voice assistants’ devices.

Fixed-size-candidate-set adaptive random testing (FSCS-ART) is an ART technique well-known for its best failure-detection effectiveness and usages in testing many real-life applications. However, it faces substantial computational overhead in terms of O(n2) time cost for generating n test inputs, which becomes worse for high dimensional input domains (number of inputs a software takes). As real-life programs generally have low failure-rates and have high dimensional input domains, it is vital to reduce the computational overhead while preserving the failure-detection effectiveness for efficient software testing. In this work, we adopted Quantization and InVerted File structure approach to enhance the original FSCS-ART, called QIVFSCS-ART. The proposed method preprocesses the software input domain by partitioning it into discrete cells by using K-means clustering using a uniform random dataset. After this, the quantized form of each executed test input is stored in the inverted list of its cell’s center, called centroid. Results show that the proposed method significantly relieves the computational overhead of FSCS-ART while preserving its failure-detection effectiveness, especially for the high-dimensional software input domains.

The Fixed-Size-Candidate-Set (FSCS) version of Adaptive Random Testing (ART) attempts to enhance the fault detection effectiveness of Random Testing (RT) by generating new test cases that are far away from previously executed test cases. Despite its simplicity and good fault-detection effectiveness, FSCS suffers from a very high time cost mainly due to its Single-Instruction-Single-Data (SISD) mechanism for its distance calculation process. To overcome this drawback, in this paper, we propose a novel and efficient implementation of FSCS, namely Fixed-Sized-Candidate-Set using Single-Instruction-Multiple-Data (FSCS-SIMD), which employs SIMD instruction architecture for simultaneous distance calculations of multiple test cases in a many-to-many style. Compared with the original FSCS, our proposed method loads a batch of multiple test cases from the candidate test case set and executed test case set in one CPU execution cycle. After that, a single distance calculation instruction is given to the whole batch for the calculation of all pairwise distances. We conducted a series of simulations and empirical studies to evaluate testing effectiveness and efficiency of our proposed method against FSCS. Our results show that, on average, FSCS-SIMD reduces test case generation overhead of FSCS up to 90%, while maintaining the comparable fault detection effectiveness.