Advances in Managing Self-Admitted Technical Debt: A Review of NLP and Machine Learning Approaches

Satya Mohan Chowdary Gorripati; Ali Altalbe; Prasanna Kumar Rangarajan

doi:10.47852/bonviewJCCE52025975

Authors

Satya Mohan Chowdary Gorripati Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham-Chennai, India https://orcid.org/0000-0002-1952-6403
Ali Altalbe Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia
Prasanna Kumar Rangarajan Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham-Chennai, India https://orcid.org/0000-0001-6103-259X

DOI:

https://doi.org/10.47852/bonviewJCCE52025975

Keywords:

Self-Admitted Technical Debt, Natural Language Processing, machine learning, automated debt detection, software maintenance, AI-driven tools

Abstract

In the evolving landscape of software engineering, managing technical debt has emerged as a critical challenge that compromises software quality and maintainability. This paper presents a structured review of recent advancements in the identification and prioritization of Self-Admitted Technical Debt (SATD) through the application of Natural Language Processing (NLP) and machine learning techniques. By synthesizing the findings from key studies, this paper highlights innovative methods that leverage word embeddings and other NLP models to enhance the automatic detection and resolution of SATD in software projects. We delve into various approaches for extracting and selecting features that accurately categorize technical debt and discuss the development of models that prioritize SATD resolution based on potential impact. Furthermore, the review also compares traditional manual strategies with automated tools, demonstrating significant improvements in efficiency and accuracy brought by AI-driven solutions. This paper aims to provide a comprehensive overview of the state-of-the-art techniques, their practical applications, and the benefits they offer to software development, fostering a deeper understanding of SATD management strategies that can lead to more sustainable software systems.

Received: 21 April 2025 | Revised: 10 September 2025 | Accepted: 7 October 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Satya Mohan Chowdary Gorripati: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Ali Altalbe: Writing – review & editing. Prasanna Kumar Rangarajan: Conceptualization, Validation, Visualization, Supervision, Project administration.