Management of Self-Admitted Technical Debt using Machine Learning

Yikun Li

Abstract:

Technical debt is a prevalent phenomenon in software development, where developers make sub-optimal decisions to achieve short-term goals, often at the expense of long-term evolvability and maintainability. These decisions, analogous to financial debt, incur principal and interest over time, which can negatively affect various aspects of software development. Therefore, effective management of technical debt is crucial to ensure software health in the long run.
This thesis focuses on identifying, analyzing, and repaying Self-Admitted Technical Debt (SATD) from various textual artifacts in software projects. We first explore SATD in issue tracking systems and then develop an integrated approach to automatically identify SATD from four sources: source code comments, issue trackers, commit messages, and pull requests. Our findings have significant implications for both practitioners and researchers and highlight the need for novel and comprehensive approaches to managing technical debt.

External IDs:doi:10.33612/diss.972242960