SATDAUG - A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt
Author:
Affiliation:
1. Bernoulli Institute, University of Groningen, Groningen, Netherlands
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3643991.3644880
Reference32 articles.
1. Multiclass Classification for Self-Admitted Technical Debt Based on XGBoost
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4. Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Zihao Wu, Lin Zhao, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, et al. 2023. Chataug: Leveraging chatgpt for text data augmentation. arXiv preprint arXiv:2302.13007 (2023).
5. Ke Dai and Philippe Kruchten. 2017. Detecting Technical Debt through Issue Trackers.. In QuASoQ@ APSEC. 59--65.
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