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A Survey of Self-Admitted Technical Debt Detection
Journal of Internet TechnologySelf-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.
Xianzhen Dou, Long Li, Yubin Qu
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On the Identification of Self-Admitted Technical Debt with Large Language Models
Anais do XXXVIII Simpósio Brasileiro de Engenharia de Software (SBES 2024)Self-Admitted Technical Debt (SATD) refers to a common practice in software engineering involving developers explicitly documenting and acknowledging technical debt within their projects. Identifying SATD in various contexts is a key activity for effective technical debt management and resolution. While previous research has focused on natural language
Pedro Lambert +2 more
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Using BiLSTM with attention mechanism to automatically detect self-admitted technical debt
Frontiers of Computer Science, 2021Dongjin Yu, Yu Dongjin
exaly
Towards the Repayment of Self-Admitted Technical Debt
2023Abdulaziz Hasan M. Alhefdhi +2 more
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An empirical study on self-admitted technical debt in modern code review
Information and Software Technology, 2022Yutaro Kashiwa +2 more
exaly
Neural Network-based Detection of Self-Admitted Technical Debt
ACM Transactions on Software Engineering and Methodology, 2019Xiaoxue Ren, ZHENCHANG Xing, Xin Xia
exaly
Using Natural Language Processing to Automatically Detect Self-Admitted Technical Debt
IEEE Transactions on Software Engineering, 2017Emad Shihab, Nikolaos Tsantalis
exaly