| 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 ASE 2019, 2019 | 114 | 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 | 83 | 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 | 70 | 2020 |
| 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 | 32 | 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 | 21 | 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 | 21 | 2021 |
| Deepgraph: A pycharm tool for visualizing and understanding deep learning models Q Hu, L Ma, J Zhao APSEC 2018, 2018 | 19 | 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 ASE 2021, 2021 | 12 | 2021 |
| 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 | 7 | 2023 |
| 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 | 6* | 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 | 4* | 2022 |
| Is Self-Attention 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 | 2 | 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 | 2* | 2022 |
| MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles Y Guo, Q Hu, M Cordy, M Papadakis, YL Traon ASE 2023 NIER, 2021 | 2 | 2021 |
| 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 | 1 | 2022 |
| Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment Q Hu, Y Guo, M Cordy, X Xie, W Ma, M Papadakis, YL Traon CAIN 2023, 2022 | 1 | 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 | | 2023 |
| CodeLens: An Interactive Tool for Visualizing Code Representations Y Guo, S Bettaieb, Q Hu, YL Traon, Q Tang arXiv preprint arXiv:2307.14902, 2023 | | 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 arXiv preprint arXiv:2306.01250, 2023 | | 2023 |
| RNNS: Representation Nearest Neighbor Search Black-Box Attack on Code Models J Zhang, W Ma, Q Hu, X Xie, YL Traon, Y Liu arXiv preprint arXiv:2305.05896, 2023 | | 2023 |
Xiaofei XieSingapore Management UniversityVerified email at smu.edu.sg
