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. 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. We developed a SATD Repayment dataset, namely SATD-R, and established a taxonomy based on the relationship and helpfulness of the SATD comment to/in repaying the debt. In addition, we developed DLRepay which takes as an input a pair of SATD comment and code, and generates a new, TD-free code. We found that there are five different categories in which the SATD comment relates to Technical Debt repayment. We also identify when the SATD comment has a positive and logical connection to repaying the debt, both generally and in every category. Furthermore, we illustrate the results of our SATD repayment approach across two datasets, three input types, two output types, and two neural networks. The resulting taxonomy of our empirical study paves the way for research to tackle further in-depth questions concerning SATD repayment comprehension, identification, and automation. In addition, the various experimental setups we conduct provide multiple insights regarding the applicability of our SATD repayment approach.

Abstract Technical Debt occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.35, 59.36, 31.78, and 583.33% improvements in terms of Precision, Recall, F-1, and Bleu-4 scores, respectively. In addition, we conduct a human evaluation to the SATD comments generated by SATDID. In 1-5 and 0–5 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively.