Current Active Learning SATD Recognition

Active learning in Self-Admitted Technical Debt (SATD) recognition focuses on efficiently identifying instances of acknowledged technical debt within software projects, leveraging diverse sources like code comments, commit messages, and issue trackers. Current research emphasizes automated identification using machine learning models, often incorporating semi-supervised or active learning strategies to minimize the need for extensive manual labeling of training data. This work aims to improve software quality and maintainability by providing developers with better tools for managing technical debt, ultimately leading to more robust and efficient software development processes.