In the landscape of systems engineering, the integration of Generative Artificial Intelligence (GenAI) offers transformative potential for Requirements Engineering (RE). This study introduces Self-Generative Requirements Engineering (Self-GenRE), a human-AI reflexive methodology leveraging GenAI with humans in the loop to facilitate continuous requirement gathering improvement in the RE process. The methodology is demonstrated through two use cases: 1) a reflexive application of GenRE to itself, utilizing NLP4ReF – a GenRE tool designed to automate system requirements generation from a partial set of initial requirements, and 2) the application of the enhanced GenRE tool to an IoT project. Self-GenRE streamlines requirement gathering, demonstrating a significant reduction in requirements gathering time from days to minutes, while increasing previously overlooked requirements identification – 42% before and 71% after enhancement. These improvements illustrate the Self-GenRE's effectiveness, enabling GenRE tools to enhance themselves based on the system of interest (SoI) and its requirements, thereby continuously improving both the process and the GenRE tools, ensuring alignment with evolving project needs. Presently, modifying GenRE tools to adhere to new requirements is performed manually; however, we envision future automation of this process, paving the way for self-improving agentic generative RE. The Self-GenRE methodology, process, and tools were developed within a Model-Based Systems Engineering (MBSE) framework, facilitating efficient design modifications. This research highlights the efficacy of integrating GenAI methodologies and processes into systems engineering, paving the way for more robust and adaptable frameworks capable of addressing the evolving challenges in modern technology development and innovation.

This paper introduces a novel System Flow Centrality (SFC) index for evaluating node importance in network graphs, with a specific focus on system architecture analysis. Unlike traditional centrality measures such as betweenness centrality (BC), the SFC index considers all system paths and cycles, providing a more comprehensive assessment of a node’s influence within the network. We present both a basic SFC algorithm and an enhanced version that incorporates system element classification, allowing for more nuanced analysis of component properties. The proposed index is validated through application to various network topologies, including common IEEE bus system configurations and a real-world medical motor driver architecture. Comparative analysis with established centrality measures demonstrates the SFC index’s superior performance in identifying critical nodes, particularly in complex and asymmetric network structures. The SFC index shows promise as a valuable tool for multidisciplinary project teams, offering potential benefits in risk assessment, resource allocation, quality management optimization, and decision-making processes across diverse applications. This research contributes to the growing body of knowledge on network analysis and system architecture evaluation, providing a more accurate and system-oriented approach to quantifying node importance in complex networks.

Many new product development (NPD) projects fail. The NPD context, which incorporates knowledge about the product, requirements, technology, and other factors, is dynamically evolving during the process. Simulations of NPD processes using their specific contexts can provide project managers with decisions-making methods to test their planning. The design structure matrix (DSM) can be used to model the product knowledge; then, reordering algorithms are used for process planning. However, interpretation of the DSM-based plan is not unique, and its translation to a process workflow (WF) model may lead to implementation inconsistencies. WF nets, being a subclass of Petri nets, provide formal tools for verifying process properties. Well handled with Regular Iteration (WRI)-WF nets are a subclass of WF nets that are sound by construction and, therefore, enable an automated process-build approach. This paper presents a formal translation of the DSM-based plan to a process-scheme model, the DSM net, which can be executed and simulated. Using several translation stages, it is proved that the resulting DSM net is equivalent to a WRI-WF net. Therefore, the proposed translation is inherently sound and can be automated, becoming an enabler of implementing evolving product knowledge into a sound changing-process model required for NPD simulations. The presented approach bridges an identified gap between the process-planning community (DSM) and the process-implementation community (Petri net).