Abstract
Technical debt management is increasingly critical in modern software systems, where organizations grapple with complex digital infrastructure. This paper aims to explore innovative data processing algorithms and frameworks that leverage generative AI to improve the diagnosis and management of technical debt problems. We conduct a literature review and synthesize findings on the application of generative AI in technical debt management, focusing on algorithmic approaches and frameworks designed for this purpose. Analysis reveals that generative AI-driven methods show promise in enabling more accurate diagnosis of technical debt, particularly in automating the identification of complex patterns and generating targeted remediation strategies.
Paper Type
Poster
DOI
10.62036/ISD.2025.17
Advanced Data Processing Algorithms and Structures for Technical Debt Management with Generative Artificial Intelligence
Technical debt management is increasingly critical in modern software systems, where organizations grapple with complex digital infrastructure. This paper aims to explore innovative data processing algorithms and frameworks that leverage generative AI to improve the diagnosis and management of technical debt problems. We conduct a literature review and synthesize findings on the application of generative AI in technical debt management, focusing on algorithmic approaches and frameworks designed for this purpose. Analysis reveals that generative AI-driven methods show promise in enabling more accurate diagnosis of technical debt, particularly in automating the identification of complex patterns and generating targeted remediation strategies.
Recommended Citation
Czyżewski, A. & Poniszewska-Maranda, A. (2025). Advanced Data Processing Algorithms and Structures for Technical Debt Management with Generative Artificial IntelligenceIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.17