| 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 | 42 | 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 | 36 | 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 TOSEM 2022, 2022 | 32 | 2022 |
| GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses W Ma, M Zhao, E Soremekun, Q Hu, J Zhang, M Papadakis, M Cordy, ... MSR 2022, 2021 | 27 | 2021 |
| 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 ASE 2021, 2021 | 19 | 2021 |
| Deepgraph: A pycharm tool for visualizing and understanding deep learning models Q Hu, L Ma, J Zhao APSEC 2018, 2018 | 19 | 2018 |
| 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 |
| 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 arXiv preprint arXiv:2212.10017, 2022 | 7* | 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 | 7* | 2022 |
| 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 |
| LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing Q Hu, Y Guo, M Cordy, X Xie, M Papadakis, YL Traon ACM Transactions on Software Engineering and Methodology, 2022 | 3 | 2022 |
| 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 | 2 | 2023 |
| Active Code Learning: Benchmarking Sample-Efficient Training of Code Models Q Hu, Y Guo, X Xie, M Cordy, L Ma, M Papadakis, YL Traon Transactions on Software Engineering, 2023 | 2 | 2023 |
Xiaofei XieSingapore Management UniversityVerified email at smu.edu.sg
