Context: Self-Admitted Technical Debt (SATD) refers to the technical debt in software that is explicitly flagged, typically by the source code comment. The SATD literature has mainly focused on comprehending, describing, detecting, and recommending SATD. Most recently, there have been efforts to study the state of the code before and after removing the SATD comment. While these efforts serve as a preliminary step towards the repayment of SATD, actual attempts towards automating SATD repayment, to the best of our knowledge, are yet to be made. Objective: In this paper, we propose the first attempt towards direct, complete, and automated SATD repayment by providing two main contributions. The first contribution is an empirical study of how the SATD comment relates to repaying the debt. The second contribution is DLRepay, our deep learning approach for SATD repayment. Method: We developed a SATD Repayment dataset, namely SATD-R, and established a taxonomy based on the relationship and hel
Self-admitted-technical-debt
Technical-debt
Deep-learning
Self-admitted-technical-debt
Software-analytics
Software-maintenance
Software-quality
Echnical-debt-repayment