| MULTI: Multi-objective effort-aware just-in-time software defect prediction X Chen, Y Zhao, Q Wang, Z Yuan Information and Software Technology 93, 1-13, 2018 | 147 | 2018 |
| Improving defect prediction with deep forest T Zhou, X Sun, X Xia, B Li, X Chen Information and Software Technology 114, 204-216, 2019 | 131 | 2019 |
| Improving high-impact bug report prediction with combination of interactive machine learning and active learning X Wu, W Zheng, X Chen, Y Zhao, T Yu, D Mu Information and Software Technology 133, 106530, 2021 | 120 | 2021 |
| Software defect number prediction: Unsupervised vs supervised methods X Chen, D Zhang, Y Zhao, Z Cui, C Ni Information and Software Technology 106, 161-181, 2019 | 114 | 2019 |
| Variable strength interaction testing with an ant colony system approach X Chen, Q Gu, A Li, D Chen 2009 16th Asia-Pacific Software Engineering Conference, 160-167, 2009 | 114 | 2009 |
| Empirical studies of a two-stage data preprocessing approach for software fault prediction W Liu, S Liu, Q Gu, J Chen, X Chen, D Chen IEEE Transactions on Reliability 65 (1), 38-53, 2015 | 101 | 2015 |
| A comprehensive study of deep learning compiler bugs Q Shen, H Ma, J Chen, Y Tian, SC Cheung, X Chen Proceedings of the 29th ACM Joint meeting on european software engineering …, 2021 | 100 | 2021 |
| A cluster based feature selection method for cross-project software defect prediction C Ni, WS Liu, X Chen, Q Gu, DX Chen, QG Huang Journal of Computer Science and Technology 32, 1090-1107, 2017 | 99 | 2017 |
| Applying particle swarm optimization to pairwise testing X Chen, Q Gu, J Qi, D Chen 2010 IEEE 34th Annual Computer Software and Applications Conference, 107-116, 2010 | 95 | 2010 |
| An empirical study on pareto based multi-objective feature selection for software defect prediction C Ni, X Chen, F Wu, Y Shen, Q Gu Journal of Systems and Software 152, 215-238, 2019 | 89 | 2019 |
| FECAR: A feature selection framework for software defect prediction S Liu, X Chen, W Liu, J Chen, Q Gu, D Chen 2014 IEEE 38th annual computer software and applications conference, 426-435, 2014 | 89 | 2014 |
| A comparative study of class rebalancing methods for security bug report classification W Zheng, Y Xun, X Wu, Z Deng, X Chen, Y Sui IEEE Transactions on Reliability 70 (4), 1658-1670, 2021 | 88 | 2021 |
| HSFal: Effective fault localization using hybrid spectrum of full slices and execution slices X Ju, S Jiang, X Chen, X Wang, Y Zhang, H Cao Journal of Systems and Software 90, 3-17, 2014 | 86 | 2014 |
| Revisiting supervised and unsupervised methods for effort-aware cross-project defect prediction C Ni, X Xia, D Lo, X Chen, Q Gu IEEE Transactions on Software Engineering 48 (3), 786-802, 2020 | 84 | 2020 |
| Interpretability application of the Just-in-Time software defect prediction model W Zheng, T Shen, X Chen, P Deng Journal of Systems and Software 188, 111245, 2022 | 78 | 2022 |
| Automated query reformulation for efficient search based on query logs from stack overflow K Cao, C Chen, S Baltes, C Treude, X Chen 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE …, 2021 | 77 | 2021 |
| DeepCount: Crowd counting with WiFi via deep learning S Liu, Y Zhao, F Xue, B Chen, X Chen arXiv preprint arXiv:1903.05316, 2019 | 70 | 2019 |
| The impact factors on the performance of machine learning-based vulnerability detection: A comparative study W Zheng, J Gao, X Wu, F Liu, Y Xun, G Liu, X Chen Journal of Systems and Software 168, 110659, 2020 | 65 | 2020 |
| Survey of static software defect prediction X Chen, Q Gu, WS Liu, S Liu, C Ni Journal of Software 27 (1), 1-25, 2016 | 65 | 2016 |
| DeepCPDP: Deep learning based cross-project defect prediction D Chen, X Chen, H Li, J Xie, Y Mu IEEE Access 7, 184832-184848, 2019 | 59 | 2019 |
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