Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Systems Engineering and Management

First Advisor

John M. Colombi, PhD

Abstract

Every acquisition program begins with a requirement, and for those programs to succeed, robust requirements engineering (RE) must be implemented. RE encompasses eliciting, analyzing, specifying, and validating requirements—a critical process throughout a program's lifecycle. Despite its importance, RE faces challenges such as scope creep, ambiguity, redundancy, and inadequate automation support, often exacerbated by reliance on historical data. To address these issues, this thesis leverages advancements in Generative Technology, particularly large language models (LLMs) such as Generative Pre-Trained Transformers (GPTs). This research developed two GPT-based tools: the Single Requirement Analysis Tool and the Set of Requirements Analysis Tool. These tools were rigorously tested against requirements data used in AFIT’s graduate courses. Results show that GPT tools achieved outputs statistically similar to those of subject matter experts (SMEs). Ultimately, the tools can perform RE analysis and specification and give users the ability to perform SME-level RE in real time. The findings highlight the transformative potential of GPTs to support Systems Engineering, showcasing measurable improvements in efficiency, accuracy, and quality compared to traditional manual methods. Integrating these tools into hybrid human-machine workflows could lead to scalable, automated RE solutions that reduce manual effort and improve engineering outcomes.

AFIT Designator

AFIT-ENV-MS-25-M-052

Comments

An embargo was observed for this posting.

Approved for public release, Distribution Unlimited. PA Case Number 88ABW-2025-0692

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