| Data quality: Some comments on the nasa software defect datasets M Shepperd, Q Song, Z Sun, C Mair IEEE Transactions on software engineering 39 (9), 1208-1215, 2013 | 586 | 2013 |
| A novel ensemble method for classifying imbalanced data Z Sun, Q Song, X Zhu, H Sun, B Xu, Y Zhou Pattern Recognition 48 (5), 1623-1637, 2015 | 465 | 2015 |
| Using coding-based ensemble learning to improve software defect prediction Z Sun, Q Song, X Zhu IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and …, 2012 | 217 | 2012 |
| Network embedding for community detection in attributed networks H Sun, F He, J Huang, Y Sun, Y Li, C Wang, L He, Z Sun, X Jia ACM Transactions on Knowledge Discovery from Data (TKDD) 14 (3), 1-25, 2020 | 93 | 2020 |
| CFPS: Collaborative filtering based source projects selection for cross-project defect prediction Z Sun, J Li, H Sun, L He Applied Soft Computing 99, 106940, 2021 | 46 | 2021 |
| Collaborative filtering based recommendation of sampling methods for software defect prediction Z Sun, J Zhang, H Sun, X Zhu Applied Soft Computing 90, 106163, 2020 | 46 | 2020 |
| An efficient destination prediction approach based on future trajectory prediction and transition matrix optimization Z Yang, H Sun, J Huang, Z Sun, H Xiong, S Qiao, Z Guan, X Jia IEEE transactions on knowledge and data engineering 32 (2), 203-217, 2018 | 25 | 2018 |
| Community search for multiple nodes on attribute graphs H Sun, R Huang, X Jia, L He, M Sun, P Wang, Z Sun, J Huang Knowledge-Based Systems 193, 105393, 2020 | 17 | 2020 |
| Automated identification of change-prone classes in open source software projects. X Zhu, Q Song, Z Sun J. Softw. 8 (2), 361-366, 2013 | 11 | 2013 |
| Leader-aware community detection in complex networks H Sun, H Du, J Huang, Y Li, Z Sun, L He, X Jia, Z Zhao Knowledge and Information Systems 62, 639-668, 2020 | 10 | 2020 |
| 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 | 8 | 2022 |
| An empirical study of software change classification with imbalance data‐handling methods X Zhu, B Niu, EJ Whitehead Jr, Z Sun Software: Practice and Experience 48 (11), 1968-1999, 2018 | 7 | 2018 |
| An empirical study of public data quality problems in cross project defect prediction Z Sun, J Li, H Sun arXiv preprint arXiv:1805.10787, 2018 | 7 | 2018 |
| An adaptive function placement in serverless computing D Xu, Z Sun Cluster Computing 25 (5), 3161-3174, 2022 | 6 | 2022 |
| Detecting semantic‐based communities in node‐attributed graphs H Sun, H Du, J Huang, Z Sun, L He, X Jia, Z Zhao Computational Intelligence 34 (4), 1199-1222, 2018 | 4 | 2018 |
| Mining cohesive clusters with interpretations in labeled graphs H Du, H Sun, J Huang, Z Sun, L He, H Cheng Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia …, 2017 | 4 | 2017 |
| An extensive empirical study of inconsistent labels in multi-version-project defect data sets S Liu, Z Guo, Y Li, C Wang, L Chen, Z Sun, Y Zhou arXiv preprint arXiv:2101.11749, 2021 | 3 | 2021 |
| Author’s Accepted Manuscript V Pullabhotla, MW Mabila Elsevier, 2016 | 3 | 2016 |
| AutoIDL: Automated Imbalanced Data Learning via Collaborative Filtering J Zhang, Z Sun, Y Qi International Conference on Knowledge Science, Engineering and Management …, 2020 | 2 | 2020 |
| A truss‐based approach for densest homogeneous subgraph mining in node‐attributed graphs H Sun, Y Zhang, X Jia, P Wang, R Huang, J Huang, L He, Z Sun Computational Intelligence 37 (2), 995-1010, 2021 | 1 | 2021 |
