| Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Y Zhou, S Liu, J Siow, X Du, Y Liu https://arxiv.org/abs/1909.03496, 2019 | 1055 | 2019 |
| Who is real bob? adversarial attacks on speaker recognition systems G Chen, S Chenb, L Fan, X Du, Z Zhao, F Song, Y Liu 2021 IEEE Symposium on Security and Privacy (SP), 694-711, 2021 | 253 | 2021 |
| Deepstellar: Model-based quantitative analysis of stateful deep learning systems X Du, X Xie, Y Li, L Ma, Y Liu, J Zhao Proceedings of the 2019 27th ACM Joint Meeting on European Software …, 2019 | 221* | 2019 |
| Leopard: Identifying vulnerable code for vulnerability assessment through program metrics X Du, B Chen, Y Li, J Guo, Y Zhou, Y Liu, Y Jiang 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE …, 2019 | 136 | 2019 |
| Towards characterizing adversarial defects of deep learning software from the lens of uncertainty X Zhang, X Xie, L Ma, X Du, Q Hu, Y Liu, J Zhao, M Sun Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 98 | 2020 |
| Coprotector: Protect open-source code against unauthorized training usage with data poisoning Z Sun, X Du, F Song, M Ni, L Li Proceedings of the ACM Web Conference 2022, 652-660, 2022 | 82 | 2022 |
| On the importance of building high-quality training datasets for neural code search Z Sun, L Li, Y Liu, X Du, L Li Proceedings of the 44th International Conference on Software Engineering …, 2022 | 80 | 2022 |
| Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions TY Zhuo, MC Vu, J Chim, H Hu, W Yu, R Widyasari, INB Yusuf, H Zhan, ... arXiv preprint arXiv:2406.15877, 2024 | 54 | 2024 |
| {FuzzJIT}:{Oracle-Enhanced} Fuzzing for {JavaScript} Engine {JIT} Compiler J Wang, Z Zhang, S Liu, X Du, J Chen 32nd USENIX Security Symposium (USENIX Security 23), 1865-1882, 2023 | 35 | 2023 |
| Vulnerability analysis, robustness verification, and mitigation strategy for machine learning-based power system stability assessment model under adversarial examples C Ren, X Du, Y Xu, Q Song, Y Liu, R Tan IEEE Transactions on Smart Grid 13 (2), 1622-1632, 2021 | 35 | 2021 |
| Codemark: Imperceptible watermarking for code datasets against neural code completion models Z Sun, X Du, F Song, L Li Proceedings of the 31st ACM Joint European Software Engineering Conference …, 2023 | 27 | 2023 |
| Decision-guided weighted automata extraction from recurrent neural networks X Zhang, X Du, X Xie, L Ma, Y Liu, M Sun Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11699 …, 2021 | 24 | 2021 |
| Marble: Model-based robustness analysis of stateful deep learning systems X Du, Y Li, X Xie, L Ma, Y Liu, J Zhao Proceedings of the 35th IEEE/ACM International Conference on Automated …, 2020 | 23 | 2020 |
| Contrastrepair: Enhancing conversation-based automated program repair via contrastive test case pairs J Kong, M Cheng, X Xie, S Liu, X Du, Q Guo arXiv preprint arXiv:2403.01971, 2024 | 17 | 2024 |
| A quantitative analysis framework for recurrent neural network X Du, X Xie, Y Li, L Ma, Y Liu, J Zhao 2019 34th IEEE/ACM International Conference on Automated Software …, 2019 | 16 | 2019 |
| Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems Z Sun, X Du, F Song, S Wang, M Ni, L Li 2023 IEEE/ACM 45th International Conference on Software Engineering …, 2023 | 15 | 2023 |
| Trace-length independent runtime monitoring of quantitative policies in LTL X Du, Y Liu, A Tiu FM 2015: Formal Methods: 20th International Symposium, Oslo, Norway, June 24 …, 2015 | 15 | 2015 |
| DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems L Wang, X Xie, X Du, M Tian, Q Guo, Z Yang, C Shen Proceedings of the 31st ACM Joint European Software Engineering Conference …, 2023 | 11 | 2023 |
| When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference Z Sun, X Du, F Song, S Wang, L Li Proceedings of the IEEE/ACM 46th International Conference on Software …, 2024 | 9 | 2024 |
| Data augmentation approaches for source code models: A survey TY Zhuo, Z Yang, Z Sun, Y Wang, L Li, X Du, Z Xing, D Lo arXiv preprint arXiv:2305.19915, 2023 | 9 | 2023 |
Yang LiuNanyang Technological University確認したメール アドレス: ntu.edu.sg
