Work in Progress: AI-Powered Engineering-Bridging Theory and Practice

TL;DR

This paper investigates how generative AI can automate and enhance systems engineering tasks, including requirement analysis, classification, and test generation, by comparing AI performance with human experts.

Contribution

It introduces a novel approach to applying generative AI for analyzing and classifying system requirements, with an emphasis on explainability and reliability in engineering contexts.

Findings

AI accurately classifies well-formed and poorly written requirements

AI provides explanations for requirement quality issues

AI shows promise in generating test specifications

Abstract

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.