A Survey of Self-Admitted Technical Debt Detection

Xianzhen Dou,
Long Li,
Yubin Qu,

Abstract


Self-Admitted Technical Debt is a key research area in the current software engineering field. By detecting Self-Admitted Technical Debt, potential bugs in software code can be detected early, thus improving software quality. We have systematically organized and analyzed SATD detection in recent years and proposed several future research directions.

Keywords


Survey, Self-admitted technical debt detection, Deep learning

Citation Format:
Xianzhen Dou, Long Li, Yubin Qu, "A Survey of Self-Admitted Technical Debt Detection," Journal of Internet Technology, vol. 26, no. 3 , pp. 379-388, May. 2025.

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