\u201cFinal Dataset of Reviewed Studies for the Systematic Literature Review on Automated SATD Detection (2015\u20132025)\u201d

This dataset accompanies the paper \u201cAutomated Detection of Self-Admitted Technical Debt: A Systematic Literature Review\u201d and provides the complete list and classification details of 46 primary studies published between 2015 and 2025.Each record includes bibliographic metadata (authors, year, venue, title), methodological attributes (input representation, detection approach, data type), and evaluation metrics extracted from the reviewed literature. The dataset is organized to support replication and meta-analysis of trends in Self-Admitted Technical Debt (SATD) research.The Excel file also includes category mappings for seven research questions (RQ1\u2013RQ7) addressed in the review, allowing researchers to explore patterns in methodology, datasets, and evaluation practices. This dataset aims to facilitate transparent, data-driven comparison of SATD detection techniques and to serve as a foundation for future empirical studies.