| Generating fluent adversarial examples for natural languages H Zhang, H Zhou, N Miao, L Li arXiv preprint arXiv:2007.06174, 2020 | 155 | 2020 |
| Generating adversarial examples for holding robustness of source code processing models H Zhang, Z Li, G Li, L Ma, Y Liu, Z Jin Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 1169-1176, 2020 | 90 | 2020 |
| Learning to represent programs with heterogeneous graphs K Zhang, W Wang, H Zhang, G Li, Z Jin Proceedings of the 30th IEEE/ACM international conference on program …, 2022 | 44 | 2022 |
| Towards robustness of deep program processing models—detection, estimation, and enhancement H Zhang, Z Fu, G Li, L Ma, Z Zhao, H Yang, Y Sun, Y Liu, Z Jin ACM Transactions on Software Engineering and Methodology (TOSEM) 31 (3), 1-40, 2022 | 23 | 2022 |
| Poison attack and defense on deep source code processing models J Li, Z Li, H Zhang, G Li, Z Jin, X Hu, X Xia arXiv preprint arXiv:2210.17029, 2022 | 14 | 2022 |
| Poison attack and poison detection on deep source code processing models J Li♂, Z Li, HZ Zhang, G Li, Z Jin, X Hu, X Xia ACM Transactions on Software Engineering and Methodology 33 (3), 1-31, 2024 | 5 | 2024 |
| Learning to represent programs with heterogeneous graphs. In 2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC) K Zhang, W Wang, H Zhang, G Li, Z Jin IEEE, 2022 | 4 | 2022 |
| CodeBERT‐Attack: Adversarial attack against source code deep learning models via pre‐trained model H Zhang, S Lu, Z Li, Z Jin, L Ma, Y Liu, G Li Journal of Software: Evolution and Process 36 (3), e2571, 2024 | 2 | 2024 |
| Selene: Pioneering Automated Proof in Software Verification L Zhang, S Lu, N Duan arXiv preprint arXiv:2401.07663, 2024 | 1 | 2024 |
| Deep-Learning-Assisted Simultaneous Target Sensing and Super-Resolution Imaging J Zhao, H Zhang, MZ Chong, YY Zhang, ZW Zhang, ZK Zhang, CH Du, ... ACS Applied Materials & Interfaces 15 (40), 47669-47681, 2023 | | 2023 |
| WELL: Applying Bug Detectors to Bug Localization via Weakly Supervised Learning Z Li, H Zhang, Z Jin, G Li arXiv preprint arXiv:2305.17384, 2023 | | 2023 |
