System-of-Systems (SoS) result from the collaboration of independent Constituent Systems (CSs), connected in a certain configuration known as an architectural configuration, to achieve a common mission. The architectural configuration may change as new CS may join SoS and/or existing CS may leave SoS at runtime. These architectural configuration changes have affect on the overall system quality attributes (i.e. performance, security and reliability). Therefore, modelling SoS architecture is a challenging task as CSs and the possible architectural configurations are not entirely known at design time. Architectural description languages (ADLs) have been proposed and used to deal with SoS dynamic architecture. However, we still envision gaps to be bridged and challenges to be addressed in the forthcoming years. This paper outline a taxonomy for modeling SoS and provides a broad discussion on the state-of-the-art modeling notations to model and analyze SoS architectural configuration. An SoS case is presented to explain the impact of architectural configurational changes on the SoS properties. Besides, we also discuss challenges and future directions to be overcome in the forthcoming years.

Over the years, knowledge on how to engineer software-intensive system-of-systems (SoS) have been expanded and advanced. With the emergence of smart cities, which is a remarkable example of SoS, there is also an emerging need that almost any type of system should be prepared to join the smart city system and become a constituent of that SoS, even temporarily. However, several attempts to build a real SoS have stumbled on the limitations of single systems, also known as Constituent System (CS), to fully interoperate in the context of a SoS. The majority of the existing systems were not designed to be part of a SoS, interoperate in a instantaneous way, or maintain their operational independence despite their participation in one or more SoS. Based on this context, the main contribution of this paper is to exploit how to design a software architecture for systems that are intended to become part of a SoS in the future. We conducted a software architecture design process to conceive a constituent architecture for Urban Mobility domain as CS. We evaluated the architecture by means of simulation. Preliminary results reveal that our proposal comply with the requirements raised during the architectural design process and can increase the possibility of a system to be a constituent of SoS.

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any potential impact on the properties critical for achieving the missions. Current modeling approaches lack the essential syntax and semantics required to model and verify SoS behaviors at design time and cannot offer alternative design choices for better design decisions. Therefore, the majority of existing techniques fail to provide qualitative and quantitative verification of SoS architecture models. Consequently, we have proposed an approach to model and verify Non-Deterministic (ND) SoS in advance by extending the current algebraic notations for the formal models as a hybrid stochastic formalism to specify and reason architectural elements with the required semantics. A formal stochastic model is developed using a hybrid approach for architectural descriptions of SoS with behavioral constraints. Through a model-driven approach, stochastic models are then translated into PRISM using formal verification rules. The effectiveness of the approach has been tested with an end-to-end case study design of an emergency response SoS for dealing with a fire situation. Architectural analysis is conducted on the stochastic model, using various qualitative and quantitative measures for SoS missions. Experimental results reveal critical aspects of SoS architecture model that facilitate better achievement of missions and QAs with improved design, using the proposed approach.

This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-Loop decision making. Existing frameworks in SOCs often focus narrowly on automation, lacking systematic structures to manage human oversight, trust calibration, and scalable autonomy with AI. Many assume static or binary autonomy settings, failing to account for the varied complexity, criticality, and risk across SOC tasks, considering human and AI collaboration. To address these limitations, we propose a novel autonomy tiered framework grounded in five levels of AI autonomy, from manual to fully autonomous, mapped to Human-in-the-Loop (HITL) roles and task-specific trust thresholds. This enables adaptive and explainable AI integration across core SOC functions, including monitoring, protection, threat detection, alert triage, and incident response. The proposed framework differentiates itself from previous research by creating formal connections between autonomy, trust, and HITL across various SOC levels, which allows for adaptive task distribution according to operational complexity and associated risks. The framework is exemplified through a simulated cyber range that features the cybersecurity AI Avatar, a RAG augmented LLM-based SOC assistant. The AI Avatar case study illustrates Human-AI collaboration for SOC tasks, reducing alert fatigue, enhancing response coordination, and strategically calibrating trust. This research systematically presents both the theoretical and practical aspects, as well as the feasibility of designing next-generation cognitive SOCs that leverage AI not to replace, but to enhance human decision-making.