Exploring the Advances in Using Machine Learning to Identify Technical Debt and Self-Admitted Technical Debt

TL;DR

This paper reviews how machine learning, especially BERT models, is used to identify technical debt and self-admitted technical debt in software engineering, highlighting current trends and future directions.

Contribution

It provides a comprehensive overview of machine learning methods for detecting technical debt, emphasizing the effectiveness of BERT models and the lack of a universally best approach.

Findings

BERT models outperform other machine learning techniques.

Performance of ML techniques has improved over the years.

No single approach is universally adopted for technical debt detection.

Abstract

In software engineering, technical debt, signifying the compromise between short-term expediency and long-term maintainability, is being addressed by researchers through various machine learning approaches. This study seeks to provide a reflection on the current research landscape employing machine learning methods for detecting technical debt and self-admitted technical debt in software projects and compare the machine learning research about technical debt and self-admitted technical debt. We performed a literature review of studies published up to 2024 that discuss technical debt and self-admitted technical debt identification using machine learning. Our findings reveal the utilization of a diverse range of machine learning techniques, with BERT models proving significantly more effective than others. This study demonstrates that although the performance of techniques has improved over the years, no universally adopted approach reigns supreme. The results suggest prioritizing BERT techniques over others in future works.