Technical debt refers to the consequences of sub-optimal decisions made during software development that prioritize short-term benefits over long-term maintainability. Self-Admitted Technical Debt (SATD) is a specific form of technical debt, explicitly documented by developers within software artifacts such as source code comments and commit messages. As SATD can hinder software development and maintenance, it is crucial to address and prioritize it effectively. However, current methodologies lack the ability to automatically estimate the repayment effort of SATD based on its textual descriptions. To address this limitation, we propose a novel approach for automatically estimating SATD repayment effort, utilizing a comprehensive dataset comprising 341,740 SATD items from 2,568,728 commits across 1,060 Apache repositories. Our findings show that different types of SATD require varying levels of repayment effort, with code/design, requirement, and test debt demanding greater effort compared to non-SATD items, while documentation debt requires less. We introduce and evaluate machine learning methodologies, particularly BERT and TextCNN, which outperforms classic machine learning methods and the naive baseline in estimating repayment effort. Additionally, we summarize keywords associated with varying levels of repayment effort that occur during SATD repayment. Our contributions aim to enhance the prioritization of SATD repayment effort and resource allocation efficiency, ultimately benefiting software development and maintainability.

自我承认技术债务（SATD）是一种特定形式的技术债务，由开发人员在软件工件中明确记录，如源代码注释和提交消息。本研究旨在提出一种新方法，利用大规模数据集自动估计SATD的还款工作，通过评估不同类型的债务及其还款的程度，并应用机器学习技术来提高还款工作的优先级和资源分配效率，以最终促进软件开发和可维护性。

自动估算偿还自我承认的技术债务所需的工作量