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.

This paper presents the results of an expert survey aimed at defining the concept of New Space. New Space is a blurry concept which is oftentimes confused with other key ideas such as the commercialization of space activities, or the use of miniaturized technology (such as CubeSats) in space missions design and implementation. Our expert survey reveals that the three key characteristics defining New Space are instead customer focus, new product development approaches, and new business models (private versus institutional investors). The paper provides a semi-quantitative comparison of these characteristics, which we define as traits, which are used to distinguish between New Space and legacy space activities. Based on the results of the comparison and literature search, we provide a brief discussion of key technology trends emerging in New Space, namely on autonomy, miniaturization, platforms, and crowd. The paper will be of relevance to all stakeholders concerned with the creation and growth of New Space ecosystems worldwide, to which we propose ideas for taking different roles in the ecosystem.

Model‐based systems engineering (MBSE) is an increasingly accepted practice in the Systems Engineering (SE) community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented for feedback to subject‐matter experts from a working group developing the first DE measurement framework. This group is a joint effort with industry, academia, and the USA government to develop DE metric standards. Once the causal map was finalized, a case study was used to partially validate the causal model. Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model. The potential metric candidates include: system quality, defects, time, rework, ease of making changes, system understanding, Effort, accessibility of information, collaboration, project methods/processes, and use of DE/MBSE tools. We believe a concerted effort across the industry to focus on measuring these variables is the most effective way to establish proof of the value of MBSE and DE.

Lessons learned through MBSE adoption efforts is one of the key ways of communicating best practices and recommendations for MBSE. This study compiles lessons learned from published case studies and practitioner interviews. Lessons are summarized into categories such as adoption strategy, modeling practices, and communication. This paper provides a source for future adopters of MBSE to review best practices and recommendations from a multitude of different experiences. This should improve the adoption and implementation of MBSE.

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.