Comprehensive Exam - Eric Melin

Presented by Eric Melin
Computing PhD, Computer Science emphasis

Location: City Center Plaza 368

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

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. I seek to provide a synthesis of 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. I performed a literature review of studies published up to 2024 that discuss technical debt and self-admitted technical debt identification using machine learning. The findings reveal the utilization of a diverse range of machine learning techniques, with BERT models proving significantly more effective than others. It also 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.

Committee: Dr. Nasir Eisty (Chair), Dr. Bogdan Dit, Dr. Francesca Spezzano, Dr. Min Long (Comp EE)