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 objective of this research article is to re-introduce some of the concepts provided by A. Wayne Wymore in his mathematical theory of Model-Based Systems Engineering, discuss why his framework might have not been adopted, and define a potential path to modernize the framework for practical application in the digital age. The dense mathematical theory has never been converted to a practical form. We propose a path to modernization by creating a metamodel of Wymore’s mathematical theory of MBSE. This enables explaining the concepts in simple to understand terms and shows the internal consistency provided by the theory. Furthermore, the metamodel allows for conversion of the theory into software application, for which we show some initial results that open the research to the art of the possible. In recognition of limitation of the theory, we make the case for a merger of the theoretical framework with the enhanced formalism of Discrete Event System Specification (DEVS). This will establish a path toward the scientific foundations for MBSE to enable future implementations of the complementary pairing and their empirical results.

The International Council on Systems Engineering (INCOSE) has initiated a Future of Systems Engineering (FuSE) program that includes a stream for advancing the theoretical foundations of the discipline of Systems Engineering (SE). A near‐term goal of FuSE is to assess the adequacy of current theoretical foundations of SE. The discipline of SE is converging toward model‐based practices (i.e., MBSE) that have not yet reached the maturity of model‐based practices in other engineering domains. For example, finite element analysis and computational fluid dynamics are grounded in mathematical theory, while, generally, MBSE is not. However, some attempts have been made to underpin MBSE with theoretical richness. This article presents a systematic literature study that surveyed state of the art on providing MBSE with mathematical foundations. Our protocol collected over 2000 publications that were reviewed for inclusion/exclusion, categorized, and analyzed. We provide insights to the type of mathematical theories used, domains of applications, and areas of SE to which the math was applied to, among other analysis. We also provide a synthesized discussion about the field moving forward, emphasizing positive trends along with the negatives and areas of concern. Overall, we found the field to be nascent.

Multi‐purpose large language models (LLMs), a subset of generative artificial intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep‐domain knowledge and operational context, raise questions regarding the efficacy of LLMs to generate SE artifacts, particularly given that they are trained using data that is broadly available on the internet. To that end, we present results from an empirical exploration, where a human expert‐generated SE artifact was taken as a benchmark, parsed, and fed into various LLMs through prompt engineering to generate segments of typical SE artifacts. This procedure was applied without any fine‐tuning or calibration to document baseline LLM performance. We then adopted a two‐fold mixed‐methods approach to compare AI generated artifacts against the benchmark. First, we quantitatively compare the artifacts using natural language processing algorithms and find that when prompted carefully, the state‐of‐the‐art algorithms cannot differentiate AI‐generated artifacts from the human‐expert benchmark. Second, we conduct a qualitative deep dive to investigate how they differ in terms of quality. We document that while the two‐material appear very similar, AI generated artifacts exhibit serious failure modes that could be difficult to detect. We characterize these as: premature requirements definition, unsubstantiated numerical estimates, and propensity to overspecify. We contend that this study tells a cautionary tale about why the SE community must be more cautious adopting AI suggested feedback, at least when generated by multi‐purpose LLMs.