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System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model generation framework leveraging Python and the spaCy NLP library to process text input and generate class/block definition diagrams using PlantUML for visual representation. The intent of this framework is to aid in reducing the manual effort in creating SysML v1.6 diagrams—class/block definition diagrams in this case. We evaluate the effectiveness of the framework using precision and recall measures. The contribution of this paper to the systems modeling domain is two-fold. First, a review and analysis of natural language processing techniques for the automated generation of SysML diagrams are provided. Second, a framework to automatically extract textual relationships tailored for generating a class diagram/block diagram that contains the classes/blocks, their relationships, methods, and attributes is presented. 

Recent studies in systems engineering indicate that the design, development, and management of systems will continue increasing in complexity. The foreseen growth is expected as future capabilities require understanding the system and its operating environment, adapting to rapid-changing scenarios, integrating more independent hardware and software elements, coordinating with multiple stakeholders across the system’s lifecycle, among others. To develop the next generation of systems, alignment between industry needs and curricula from higher-education institutions should exist. Therefore, this research contributes to the human capital development of systems engineering in the United States by exploring current job opportunities and their relationship to existing academic offerings in Hispanic-Serving Institutions. The study analyzes job openings from INCOSE’s CAB Members to capture current needs in terms of role description lifecycle experience, tools and methodologies needed in the job market, and it explores the relationship of systems engineering methodologies covered in Hispanic-Serving Institutions. The outcome of this research provides a direction to support the development, adoption, and update of higher-education systems engineering curriculum that aligns with current industry needs. 

Model-Based Systems Engineering (MBSE) supports the development of complex systems through capturing, communicating, and managing system specifications with an emphasis on the use of modeling languages, tools, and methods. It is a well-known fact that varying levels of effort are required to implement MBSE in industries based on the complexity of the systems a given industry is associated with. This paper shares the results of a survey to industry professionals from Defense, Aerospace, Automotive, Consultancy, Software, and IT industry clusters. The research goal is to understand the current state of perception on what MBSE is and the use of MBSE among different industry clusters. The survey analysis includes a comparison of how MBSE is defined, advantages on the use of MBSE, project types, specific life cycle stage when MBSE is applied, and adoption challenges, as reported by the survey participants. The researchers also aim to trigger discussions in the MBSE community for identifying strategies to address MBSE related challenges tailored to a specific industry type. 

Academia or workforce development workshops can both increase the plausibility of a streamlined transition from a document-centric approach to MBSE frameworks, and aid the integration of Model-Based Systems Engineering (MBSE) within the current industry and the challenges faced, introducing MBSE concepts, tools, and languages. This paper reports on an online model-based system engineering Bootcamp conducted in collaboration with The University of Texas Rio Grande Valley and The University of Texas at El Paso. The importance of MBSE is emphasized throughout the online Bootcamp to a diverse group of audience i.e., students, faculty, and industry professionals unfamiliar with systems engineering. A set of predefined questions through pre and post Bootcamp surveys aided in determining the perceptions of MBSE and the effectiveness of the Bootcamp in increasing the knowledge of MBSE amongst participants. A positive knowledge gain was observed on the importance of systems modeling and MBSE across students, faculty, and industry personnel participants indicating the effectiveness of the online Bootcamp. A set of open-ended questions were targeted specifically for industry professionals from manufacturing, aerospace, healthcare, transportation, and software domains attending the Bootcamp for capturing the perceived challenges and obstacles according to them for implementing Model-Based Systems Engineering in their organizations.