Traditional document‐based practices in systems engineering are being transitioned to model‐based ones. Adoption of model‐based systems engineering (MBSE) continues to grow in industry and government, and MBSE continues to be a major research theme in the systems engineering community. In fact, MBSE remains a central element in the International Council on Systems Engineering (INCOSE)’s vision for 2025. Examining systems engineering literature, this paper presents an assessment of the extent to which benefits and value of MBSE are supported by empirical evidence. A systematic review of research and practice papers in major systems engineering archival journals and conference proceedings was conducted. Evidence was categorized in four types, two of which inductively emerged from the results: measured, observed (without a formal measurement process), perceived (claimed without evidence), and backed by other references. Results indicate that two thirds of claimed MBSE benefits are only supported by perceived evidence, while only two papers reported measured evidence. The aggregate assessment presented in this paper indicates that claims about the value and benefits of MBSE are mainly based on expectation. We argue that evidence supporting the value and benefits of MBSE remains inconclusive.

The field of systems engineering has recently experienced a new push for unveiling its scientific foundations and using them to inform better practice. The majority of the research effort towards a theory of systems engineering has concentrated on the early phases of the system's lifecycle, especially in the areas of problem formulation and system architecture and design. However, and despite their importance for system success, the design of verification strategies has received little attention. Current work is of procedural nature, providing guidance instead of enabling computation, or is specific to a particular verification case. As a result, the definition of verification strategies in practice continues to be driven by heuristics and best practices. This has shown to be suboptimal. In order to fill in this gap, this paper contributes to the theory of systems engineering with a mathematical model of verification strategies. The mathematical model is generic, capturing verification comprehensively, and enables computation. First, a descriptive case is presented to facilitate understanding how the mathematical model relates to practice. Second, a quantitative case is presented to justify the need of the model. K E Y W O R D Ssystem modeling, verification and validation

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This paper proposes a set of seven elemental patterns of verification strategies. These patterns can be useful in modeling verification strategies in a wide range of engineered systems. They form the building blocks under which any verification strategy can be modeled. The patterns lead to a fundamental understanding of the interplay between system parameters and verification activities, as well as an understanding of the mechanisms by which verification evidence builds up. For each pattern, we provide a description and a few examples of its application. A few important theoretical properties of the corresponding set of patterns are discussed, such as belief update, inferential properties, and graph disconnection, as well as some practical guidance to be taken into account when applying them to authentic verification problems. These patterns are intended to be a useful tool for researchers, practitioners, and educators, by formalizing the application of Bayesian networks to verification problems, hence facilitating instruction and communication among verification engineers and with researchers from other domains, particularly statisticians and Bayesian analysts.

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.

Some authors suggest that transitioning requirements engineering from the traditional statements in natural language with shall clauses to model-based requirements within a Model-Based Systems Engineering (MBSE) environment could improve communication, requirements traceability, and system decomposition, among others. Requirement elements in the Systems Modeling Language (SysML) fail to fulfill this objective, as they are really a textual requirement in natural language as a model element. Current efforts to directly leverage behavioral and structural models of the system lack an overarching theoretical framework with which to assess the adequacy of how those models are used to capture requirements. This paper presents an approach to construct true model-based requirements in SysML. The presented approach leverages some of SysML’s behavioral and structural models and diagrams, with specific construction rules derived from Wymore’s mathematical framework for MBSE and taxonomies of requirements and interfaces. The central proposition of the approach is that every requirement can be modeled as an input/output transformation. Examples are used to show how attributes traditionally thought of as non-functional requirements can be captured, with higher precision, as functional transformations.

The literature shows disparities in how fundamental systems engineering concepts in the area of requirements engineering, such as stakeholder needs, system requirements, requirements elicitation, requirements derivation, and requirements decomposition, are used within the communities‐of‐practice and in research. Such disparities can lead to conceptual and application inconsistencies, which have been shown to contribute to the formulation of poor requirements. In this paper, such concepts are articulated using systems theory as the underlying theoretical framework. The concepts of problem space, solution space, open system, and closed system are central to this work. It is argued that the proposed articulations facilitate avoiding usage disparity, ultimately resulting in better formulation of requirements. These articulations are supported by in‐depth examples that comprehensively cover different types of needs and requirements, and provide step‐by‐step insights into how elicitation, derivation, and decomposition occur within a problem formulation effort.

In systems engineering, verification activities evaluate the extent to which a system under development satisfies its requirements. In large systems engineering projects, multiple firms are involved in the system development, and hence verification activities must be coordinated. Self-interest impedes the implementation of verification strategies that are beneficial for all firms while encouraging each firm to choose a verification strategy beneficial to itself. Incentives for verification activities can motivate a single firm to adopt verification strategies beneficial to all firms in the project, but these incentives must be offered judiciously to minimize unnecessary expenditures and prevent the abuse of goodwill. In this paper, we use game theory to model a contractor-subcontractor scenario, in which the subcontractor provides a component to the contractor, who further integrates it into their system. Our model uses belief distributions to capture each firm’s epistemic uncertainty in their component’s state prior to verification, and we use multiscale decision theory to model interdependencies between the contractor and subcontractor’s design. We propose an incentive mechanism that aligns the verification strategies of the two firms and using our game-theoretic model, we identify those scenarios where the contractor benefits from incentivizing the subcontractor’s verification activities.