  | HU, Q.* , WEN, J.* , Zhang, Y., CORDY, M., & Lyu, Y. (2026). On the Evaluation of Capability Estimation Methods for Large Language Models. In S. Koenig, C. Jenkins, ... M. E. Taylor (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence. doi:10.1609/aaai.v40i37.40368  Peer reviewed* These authors have contributed equally to this work. |
 | HU, Q., Guo, Y., Xie, X., CORDY, M., Ma, W., PAPADAKIS, M., Ma, L., & LE TRAON, Y. (14 August 2025). Assessing the Robustness of Test Selection Methods for Deep Neural Networks. ACM Transactions on Software Engineering and Methodology, 34 (7). doi:10.1145/3715693 Peer Reviewed verified by ORBi |
 | WEN, J., HU, Q., GUO, Y., CORDY, M., & Le Traon, Y. (2025). Variable Renaming-Based Adversarial Test Generation for Code Model: Benchmark and Enhancement. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3723353 Peer Reviewed verified by ORBi |
 | DONG, Z., HU, Q., GUO, Y., Zhang, Z., CORDY, M., PAPADAKIS, M., Le Traon, Y., & Zhao, J. (18 February 2025). Boosting source code learning with text-oriented data augmentation: an empirical study. Empirical Software Engineering, 30 (3). doi:10.1007/s10664-025-10624-2 Peer Reviewed verified by ORBi |
 | DONG, Z., HU, Q., Zhang, Z., GUO, Y., CORDY, M., PAPADAKIS, M., Traon, Y. L., & Zhao, J. (October 2024). On the effectiveness of hybrid pooling in mixup-based graph learning for language processing. Journal of Systems and Software, 216, 112139. doi:10.1016/j.jss.2024.112139 Peer Reviewed verified by ORBi |
 | HU, Q., GUO, Y., Xie, X., CORDY, M., Ma, L., PAPADAKIS, M., & Traon, Y. L. (May 2024). Active Code Learning: Benchmarking Sample-Efficient Training of Code Models. IEEE Transactions on Software Engineering, 50 (5), 1080 - 1095. doi:10.1109/TSE.2024.3376964 Peer Reviewed verified by ORBi |
 | GUO, Y., HU, Q., Xie, X., CORDY, M., PAPADAKIS, M., & Le Traon, Y. (16 January 2024). KAPE: <i>k</i> NN-Based Performance Testing for Deep Code Search. ACM Transactions on Software Engineering and Methodology, 33 (2), 48:1-48:24. doi:10.1145/3624735 Peer Reviewed verified by ORBi |
 | HU, Q., Yuejun Guo, Xiaofei Xie, CORDY, M., Lei Ma, PAPADAKIS, M., & LE TRAON, Y. (2024). Test Optimization in DNN Testing: A Survey. ACM Transactions on Software Engineering and Methodology, 33 (4), 111:1-111:42. doi:10.1145/3643678 Peer Reviewed verified by ORBi |
 | HU, Q., GUO, Y., Xie, X., CORDY, M., PAPADAKIS, M., & Le Traon, Y. (January 2024). 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 |
 | HU, Q. (2023). Label-Efficient Deep Learning Engineering [Doctoral thesis, SnT]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/58971 |
  | 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. doi:10.48550/arXiv.2308.01314 |
 | 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 verified by ORBi |
 | 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 |