Profil

HU Qiang

Main Referenced Co-authors
CORDY, Maxime  (13)
PAPADAKIS, Michail  (12)
GUO, Yuejun  (10)
Xie, Xiaofei (8)
LE TRAON, Yves  (6)
Main Referenced Keywords
Software (3); Active Learning (1); Active learning (1); active learning (1); Adversarial robustness (1);
Main Referenced Disciplines
Computer science (15)

Publications (total 15)

The most downloaded
852 downloads
HU, Q., GUO, Y., CORDY, M., Xiaofei, X., MA, W., PAPADAKIS, M., & LE TRAON, Y. (2021). Towards Exploring the Limitations of Active Learning: An Empirical Study. In The 36th IEEE/ACM International Conference on Automated Software Engineering. doi:10.1109/ASE51524.2021.9678672 https://hdl.handle.net/10993/48351

The most cited

20 citations (Scopus®)

HU, Q., GUO, Y., CORDY, M., Xie, X., Ma, L., PAPADAKIS, M., & LE TRAON, Y. (2022). An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3511598 https://hdl.handle.net/10993/50265

HU, Q., Yuejun Guo, Xiaofei Xie, CORDY, M., Lei Ma, PAPADAKIS, M., & LE TRAON, Y. (In press). Test Optimization in DNN Testing: A Survey. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/59273.

HU, Q. (2023). Label-Efficient Deep Learning Engineering [Doctoral thesis, SnT]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/58971

HU, Q., GUO, Y., Xie, X., CORDY, M., PAPADAKIS, M., & Le Traon, Y. (24 November 2023). LaF: Labeling-free Model Selection for Automated Deep Neural Network Reusing. ACM Transactions on Software Engineering and Methodology, 33 (1), 1-28. doi:10.1145/3611666
Peer Reviewed verified by ORBi

GUO, Y., HU, Q., Xie, X., CORDY, M., PAPADAKIS, M., & Le Traon, Y. (2023). KAPE: <i>k</i> NN-Based Performance Testing for Deep Code Search. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3624735
Peer Reviewed verified by ORBi

Dong, Z., HU, Q., Zhang, Z., & Zhao, J. (2023). On the Effectiveness of Graph Data Augmentation for Source Code Learning. In 2023 10th International Conference on Dependable Systems and Their Applications (DSA). Tokyo, Japan: IEEE. doi:10.1109/dsa59317.2023.00124
Peer reviewed

HU, Q., Guo, Y., Xie, X., CORDY, M., Ma, W., PAPADAKIS, M., & LE TRAON, Y. (2023). Evaluating the Robustness of Test Selection Methods for Deep Neural Networks. preprint.

GUO, Y., HU, Q., CORDY, M., Papadakis, M., & Le Traon, Y. (February 2023). DRE: density-based data selection with entropy for adversarial-robust deep learning models. Neural Computing and Applications, 35 (5), 4009 - 4026. doi:10.1007/s00521-022-07812-2
Peer Reviewed verified by ORBi

HU, Q., GUO, Y., Xie, X., CORDY, M., PAPADAKIS, M., Ma, L., & Traon, Y. (2023). Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation. 45th IEEE/ACM International Conference on Software Engineering (ICSE), 1776–1787. doi:10.1109/ICSE48619.2023.00152
Peer reviewed

HU, Q., GUO, Y., CORDY, M., Xie, X., MA, W., PAPADAKIS, M., & Traon, Y. (2023). Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment. 2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023, 56–67. doi:10.1109/CAIN58948.2023.00015
Peer reviewed

HU, Q., GUO, Y., CORDY, M., PAPADAKIS, M., & Traon, Y. (2023). MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack. 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), 1708–1712. doi:10.1109/ASE56229.2023.00042
Peer reviewed

HU, Q., GUO, Y., Xie, X., CORDY, M., PAPADAKIS, M., Ma, L., & LE TRAON, Y. (2023). CodeS: Towards Code Model Generalization Under Distribution Shift. IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results, 1–6. doi:10.1109/ICSE-NIER58687.2023.00007
Peer reviewed

Dong, Z., HU, Q., GUO, Y., CORDY, M., PAPADAKIS, M., Zhang, Z., LE TRAON, Y., & Zhao, J. (2023). MixCode: Enhancing Code Classification by Mixup-Based Data Augmentation. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 379–390. doi:10.1109/SANER56733.2023.00043
Peer reviewed

MA, W., Zhao, M., SOREMEKUN, E., HU, Q., Zhang, J. M., PAPADAKIS, M., CORDY, M., Xie, X., & Traon, Y. L. (2022). GraphCode2Vec: generic code embedding via lexical and program dependence analyses. In Proceedings of the 19th International Conference on Mining Software Repositories (pp. 524--536). doi:10.1145/3524842.3528456
Peer reviewed

HU, Q., GUO, Y., CORDY, M., Xie, X., Ma, L., PAPADAKIS, M., & LE TRAON, Y. (2022). An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3511598
Peer reviewed

HU, Q., GUO, Y., CORDY, M., Xiaofei, X., MA, W., PAPADAKIS, M., & LE TRAON, Y. (2021). Towards Exploring the Limitations of Active Learning: An Empirical Study. In The 36th IEEE/ACM International Conference on Automated Software Engineering. doi:10.1109/ASE51524.2021.9678672
Peer reviewed

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