| How far have we progressed in identifying self-admitted technical debts? A comprehensive empirical study Z Guo, S Liu, J Liu, Y Li, L Chen, H Lu, Y Zhou ACM Transactions on Software Engineering and Methodology (TOSEM) 30 (4), 1-56, 2021 | 46 | 2021 |
| Mitigating false positive static analysis warnings: Progress, challenges, and opportunities Z Guo, T Tan, S Liu, X Liu, W Lai, Y Yang, Y Li, L Chen, W Dong, Y Zhou IEEE Transactions on Software Engineering, 2023 | 14 | 2023 |
| MAT: A simple yet strong baseline for identifying self-admitted technical debt Z Guo, S Liu, J Liu, Y Li, L Chen, H Lu, Y Zhou, B Xu arXiv preprint arXiv:1910.13238, 2019 | 14 | 2019 |
| Inconsistent defect labels: Essence, causes, and influence S Liu, Z Guo, Y Li, C Wang, L Chen, Z Sun, Y Zhou, B Xu IEEE Transactions on Software Engineering 49 (2), 586-610, 2022 | 12 | 2022 |
| Boosting crash-inducing change localization with rank-performance-based feature subset selection Z Guo, Y Li, W Ma, Y Zhou, H Lu, L Chen, B Xu Empirical Software Engineering 25, 1905-1950, 2020 | 7 | 2020 |
| A Survey on Modern Code Review: Progresses, Challenges and Opportunities Z Yang, C Gao, Z Guo, Z Li, K Liu, X Xia, Y Zhou arXiv preprint arXiv:2405.18216, 2024 | 6 | 2024 |
| Code-line-level bugginess identification: How far have we come, and how far have we yet to go? Z Guo, S Liu, X Liu, W Lai, M Ma, X Zhang, C Ni, Y Yang, Y Li, L Chen, ... ACM Transactions on Software Engineering and Methodology 32 (4), 1-55, 2023 | 6 | 2023 |
| Prioritizing code documentation effort: Can we do it simpler but better? S Liu, Z Guo, Y Li, H Lu, L Chen, L Xu, Y Zhou, B Xu Information and Software Technology 140, 106686, 2021 | 5 | 2021 |
| 基于信息检索的缺陷定位: 问题, 进展与挑战 郭肇强， 周慧聪， 刘释然， 李言辉， 陈林， 周毓明， 徐宝文 软件学报 31 (9), 2826-2854, 2020 | 3 | 2020 |
| iSMELL: Assembling LLMs with Expert Toolsets for Code Smell Detection and Refactoring D Wu, F Mu, L Shi, Z Guo, K Liu, W Zhuang, Y Zhong, L Zhang Proceedings of the 39th IEEE/ACM International Conference on Automated …, 2024 | 2 | 2024 |
| Towards a framework for reliable performance evaluation in defect prediction X Liu, S Liu, Z Guo, P Zhang, Y Yang, H Liu, H Lu, Y Li, L Chen, Y Zhou Science of Computer Programming, 103164, 2024 | 1 | 2024 |
| Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-Analysis Y Mei, Y Rong, S Liu, Z Guo, Y Yang, H Lu, Y Tang, Y Zhou International Journal of Software Engineering and Knowledge Engineering 33 …, 2023 | 1 | 2023 |
| Deep learning or classical machine learning? An empirical study on line‐level software defect prediction Y Zhou, X Liu, Z Guo, Y Zhou, C Zhang, J Qian Journal of Software: Evolution and Process 36 (10), e2696, 2024 | | 2024 |
| Toward a consistent performance evaluation for defect prediction models X Liu, S Liu, Z Guo, P Zhag, Y Yang, H Liu, H Lu, Y Li, L Chen, Y Zhou arXiv preprint arXiv:2302.00394, 2023 | | 2023 |
| 面向对象软件度量阈值的确定方法: 问题, 进展与挑战 梅元清， 郭肇强， 周慧聪， 李言辉， 陈林， 卢红敏， 周毓明 软件学报 34 (1), 50-102, 2021 | | 2021 |
| 自承认技术债的研究: 问题, 进展与挑战 郭肇强， 刘释然， 谭婷婷， 李言辉， 陈林， 周毓明， 徐宝文 Workshop on Managing Technical Debt) 来跟进技术债相关的研究 10, 17, 2021 | | 2021 |