Self-Generative Requirements Engineering

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.