What is it about?
Self-Admitted Technical Debt (SATD) is the Technical Debt expressed through code comments and issue tracker. In this paper, we propose an automated detection approach of SATD that at first detect the debt comments and then classify these comments into debt types (i.e. defect, documentation, test, design, and requirement). This approach is embedded into a user-friendly tool.
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Why is it important?
We propose the first two-step classification approach able to detect SATD. We also propose the first tool that implements this task.
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This page is a summary of: DebtHunter: A Machine Learning-based Approach for Detecting Self-Admitted Technical Debt, June 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3463274.3464455.
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