2024/09/06 by Eric L. Melin, Melin, Eric L., Nasir U. Eisty +1 · 1 citation
Business, Management and Accounting · Decision Sciences · #FOS: Computer and information sciences #FinTech, Crowdfunding, Digital Finance #Financial Distress and Bankruptcy Prediction #Software Engineering (cs.SE) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2409.04662
openalex publication_date 2024/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.