Runtime composition in dynamic system of systems: A systematic review of challenges, solutions, tools, and evaluation methods

• 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.

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Publication Details

Journal
Journal of Systems and Software
Published
2025-10-24
DOI
https://doi.org/10.1016/j.jss.2025.112661
Primary Topic
Systems Engineering Methodologies and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Runtime composition in dynamic system of systems: A systematic review of challenges, solutions, tools, and evaluation methods

Muhammad Waseem, Muhammad Ashfaq, Niko Mäkitalo, Ahmed R. Sadik et al.
Journal of Systems and Software
Systems Engineering Methodologies and Applications
article

Runtime composition in dynamic system of systems: A systematic review of challenges, solutions, tools, and evaluation methods

Muhammad Waseem, Muhammad Ashfaq, Niko Mäkitalo, Ahmed R. Sadik, Tommi Mikkonen, Teerath Das
article en

Abstract

• 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.

Journal of Systems and SoftwareVol. 232
Tampere University of Applied Sciences (FI), Honda (Germany) (DE), Tampere University (FI), University of Jyväskylä (FI)
Openalex Percentile: Top 61%
Systems Engineering Methodologies and Applications
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