| An empirical study towards characterizing deep learning development and deployment across different frameworks and platforms. Q Guo, S Chen, X Xie, L Ma, Q Hu, H Liu, Y Liu, J Zhao, X Li Proceedings of the 34th IEEE/ACM International Conference on Automated …, 2019 | 126 | 2019 |
| DeepMutation++: A mutation testing framework for deep learning systems Q Hu, L Ma, X Xie, B Yu, Y Liu, J Zhao ASE 2019, 2019 | 97 | 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 ICSE 2020, 2020 | 80 | 2020 |
| The scope of chatgpt in software engineering: A thorough investigation W Ma, S Liu, W Wang, Q Hu, Y Liu, C Zhang, L Nie, Y Liu arXiv preprint arXiv:2305.12138, 2023 | 45 | 2023 |
| Secure deep learning engineering: A software quality assurance perspective L Ma, F Juefei-Xu, M Xue, Q Hu, S Chen, B Li, Y Liu, J Zhao, J Yin, S See arXiv preprint arXiv:1810.04538, 2018 | 37 | 2018 |
| An empirical study on data distribution-aware test selection for deep learning enhancement Q Hu, Y Guo, M Cordy, X Xie, L Ma, M Papadakis, Y Le Traon ACM Transactions on Software Engineering and Methodology (TOSEM) 31 (4), 1-30, 2022 | 32 | 2022 |
| Graphcode2vec: Generic code embedding via lexical and program dependence analyses W Ma, M Zhao, E Soremekun, Q Hu, JM Zhang, M Papadakis, M Cordy, ... Proceedings of the 19th International Conference on Mining Software …, 2022 | 28 | 2022 |
| Deepgraph: A pycharm tool for visualizing and understanding deep learning models Q Hu, L Ma, J Zhao APSEC 2018, 2018 | 21 | 2018 |
| Towards exploring the limitations of active learning: An empirical study Q Hu, Y Guo, M Cordy, X Xie, W Ma, M Papadakis, Y Le Traon 2021 36th IEEE/ACM International Conference on Automated Software …, 2021 | 19 | 2021 |
| DRE: density-based data selection with entropy for adversarial-robust deep learning models Y Guo, Q Hu, M Cordy, M Papadakis, Y Le Traon Neural Computing and Applications 35 (5), 4009-4026, 2023 | 10* | 2023 |
| CodeS: Towards Code Model Generalization Under Distribution Shift Q Hu, Y Guo, X Xie, M Cordy, L Ma, M Papadakis, YL Traon ICSE 2023 NIER, 2022 | 10* | 2022 |
| MixCode: Enhancing Code Classification by Mixup-Based Data Augmentation Z Dong, Q Hu, Y Guo, M Cordy, M Papadakis, YL Traon, J Zhao SANER 2023, 2022 | 8* | 2022 |
| Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation Q Hu, Y Guo, X Xie, M Cordy, L Ma, M Papadakis, YL Traon ICSE 2023, 2022 | 8* | 2022 |
| Boosting source code learning with data augmentation: An empirical study Z Dong, Q Hu, Y Guo, Z Zhang, M Cordy, M Papadakis, YL Traon, J Zhao arXiv preprint arXiv:2303.06808, 2023 | 7 | 2023 |
| Are Code Pre-trained Models Powerful to Learn Code Syntax and Semantics? W Ma, M Zhao, X Xie, Q Hu, S Liu, J Zhang, W Wang, Y Liu ACM Transactions on Software Engineering and Methodology, 2022 | 7* | 2022 |
| LaF: labeling-free model selection for automated deep neural network reusing Q Hu, Y Guo, X Xie, M Cordy, M Papadakis, Y Le Traon ACM Transactions on Software Engineering and Methodology 33 (1), 1-28, 2023 | 4 | 2023 |
| Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment Q Hu, Y Guo, M Cordy, X Xie, W Ma, M Papadakis, Y Le Traon 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software …, 2023 | 4* | 2023 |
| MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack Q Hu, Y Guo, M Cordy, M Papadakis, Y Le Traon 2023 38th IEEE/ACM International Conference on Automated Software …, 2023 | 3* | 2023 |
| Evaluating the robustness of test selection methods for deep neural networks Q Hu, Y Guo, X Xie, M Cordy, W Ma, M Papadakis, YL Traon arXiv preprint arXiv:2308.01314, 2023 | 3 | 2023 |
| Test optimization in DNN testing: a survey Q Hu, Y Guo, X Xie, M Cordy, L Ma, M Papadakis, Y Le Traon ACM Transactions on Software Engineering and Methodology 33 (4), 1-42, 2024 | 2 | 2024 |
Xiaofei XieSingapore Management UniversityVerified email at smu.edu.sg
