An AI-Enhanced Technical Debt Management Framework for Aerospace and Defense Systems Engineering: Framework Design and Illustrative Application

Technical debt (TD) poses a significant systemic risk in aerospace systems engineering, yet existing frameworks inadequately address debt irreversibility at hardware–software integration boundaries. Current detection approaches operate on structured code artifacts rather than the unstructured test and evaluation (T&E) documentation where integration debt often becomes visible. This paper presents the Technical Debt Management Framework (TDMF), a proof-of-concept architecture for identifying, quantifying, and prioritizing TD across the systems engineering lifecycle. The TDMF proposes an integrative architecture combining leading indicator (LI) monitoring with an AI detection module using large language model (LLM) analysis to surface debt indicators within unstructured aerospace documentation. The framework is grounded in a systematic review of 143 publications and illustrated through retrospective application to the Hubble Space Telescope and Mars Climate Orbiter failures, with an Evidence Traceability Matrix bounding historical claims against hindsight bias. An initial pilot evaluation of the ATLAS prototype—conducted on a single-program aerospace T&E documentation using GPT-4 with expert annotation—yielded a preliminary F1 score of 0.82 and an observed 45% reduction in median review time, providing initial evidence of computational feasibility within that scope. The framework is positioned as an early-stage design-science artifact at Technology Readiness Level 2–3. Prospective multi-program validation constitutes the required next study. This work contributes a proof-of-concept management architecture, a documented prompt engineering approach for TD classification, and a structured research agenda for empirical validation for TD classification in mission-critical systems engineering.