| 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 | 105 | 2019 |
| 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 | 101 | 2019 |
| Defect detection of industry wood veneer based on NAS and multi-channel mask R-CNN J Shi, Z Li, T Zhu, D Wang, C Ni Sensors 20 (16), 4398, 2020 | 97 | 2020 |
| 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 | 91 | 2017 |
| Survey of static software defect prediction 陈翔， 顾庆， 刘望舒， 刘树龙， 倪超 Journal of Software 27 (1), 1-25, 2015 | 79 | 2015 |
| 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 | 77 | 2020 |
| A survey on cross-project software defect prediction methods X Chen, LP Wang, Q Gu, Z Wang, C Ni, WS Liu, Q Wang Chinese Journal of Computers 41 (1), 254-274, 2018 | 41 | 2018 |
| The best of both worlds: integrating semantic features with expert features for defect prediction and localization C Ni, W Wang, K Yang, X Xia, K Liu, D Lo Proceedings of the 30th ACM Joint European Software Engineering Conference …, 2022 | 36 | 2022 |
| FeSCH: a feature selection method using clusters of hybrid-data for cross-project defect prediction C Ni, W Liu, Q Gu, X Chen, D Chen 2017 IEEE 41st annual computer software and applications conference (COMPSAC …, 2017 | 36 | 2017 |
| Revisiting heterogeneous defect prediction methods: How far are we? X Chen, Y Mu, K Liu, Z Cui, C Ni Information and Software Technology 130, 106441, 2021 | 27 | 2021 |
| Do different cross‐project defect prediction methods identify the same defective modules? X Chen, Y Mu, Y Qu, C Ni, M Liu, T He, S Liu Journal of Software: Evolution and Process 32 (5), e2234, 2020 | 22 | 2020 |
| Just-In-Time Defect Prediction on JavaScript Projects: A Replication Study C NI, XIN XIA, D LO, X YANG, AE HASSAN | 18 | 2021 |
| Cross-project defect prediction method based on feature transfer and instance transfer 倪超， 陈翔， 刘望舒， 顾庆， 黄启国， 李娜 Journal of Software 30 (5), 1308-1329, 2019 | 18 | 2019 |
| Defect identification, categorization, and repair: Better together C Ni, K Yang, X Xia, D Lo, X Chen, X Yang arXiv preprint arXiv:2204.04856, 2022 | 10 | 2022 |
| Multitask defect prediction C Ni, X Chen, X Xia, Q Gu, Y Zhao Journal of Software: Evolution and Process 31 (12), e2203, 2019 | 9 | 2019 |
| 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 | 4 | 2023 |
| Multi-project Regression based Approach for Software Defect Number Prediction. Q Huang, C Ni, X Chen, Q Gu, K Cao SEKE, 425-546, 2019 | 4 | 2019 |
| Boosting Just-in-Time Defect Prediction with Specific Features of C/C++ Programming Languages in Code Changes C Ni, X Xu, K Yang, D Lo 2023 IEEE/ACM 20th International Conference on Mining Software Repositories …, 2023 | 3 | 2023 |
| FVA: Assessing Function-Level Vulnerability by Integrating Flow-Sensitive Structure and Code Statement Semantic C Ni, L Shen, W Wang, X Chen, X Yin, L Zhang 2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC …, 2023 | 3 | 2023 |
| Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network Z Liu, L Zhao, X Yu, Y Zhang, J Cui, C Ni, L Zhang Science Progress 105 (4), 00368504221137461, 2022 | 2 | 2022 |
Xiang Chen （陈翔）Associate Professor, Nantong University, China在 ntu.edu.cn 的电子邮件经过验证
