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.

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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

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.