  | 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 |
DOGOULIS, P. T., JIMENEZ, M., GHAMIZI, S., CORDY, M., & LE TRAON, Y. (28 July 2024). Robustness Analysis of AI Models in Critical Energy Systems [Poster presentation]. International Conference on Machine Learning, Vienna, Austria. Peer reviewed |
 | DANG, X., LI, Y., Wei Ma, Yuejun Guo, Qiang Hu, PAPADAKIS, M., CORDY, M., & LE TRAON, Y. (22 July 2024). Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study. Empirical Software Engineering, 29 (5). doi:10.1007/s10664-024-10515-y 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 |
 | ZEYEN, O., CORDY, M., Perrouin, G., & Acher, M. (14 April 2024). Exploring the Computational Complexity of SAT Counting and Uniform Sampling with Phase Transitions [Poster presentation]. Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, Lisbon, Prt. doi:10.1145/3639478.3643097 Peer reviewed |
 | LAZREG, S., CORDY, M., HANSEN, S. T., & Legay, A. (2024). Daedalux: An Extensible Platform for Variability-Aware Model Checking. In Proceedings - 2024 ACM/IEEE 46th International Conference on Software Engineering: Companion, ICSE-Companion 2024 (pp. 21-25). IEEE Computer Society. doi:10.1145/3639478.3640043 Peer reviewed |
 | ZEYEN, O., CORDY, M., Perrouin, G., & Acher, M. (2024). Preprocessing is What You Need: Understanding and Predicting the Complexity of SAT-based Uniform Random Sampling. In Proceedings - 2024 IEEE/ACM 12th International Conference on Formal Methods in Software Engineering, FormaliSE 2024. Association for Computing Machinery, Inc. doi:10.1145/3644033.3644371 Peer reviewed |
 | 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 |
 | SIMONETTO, T. J. A., GHAMIZI, S., & CORDY, M. (2024). Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data. In Proceedings of The Thirty-Eighth Annual Conference on Neural Information Processing Systems. TBD. Peer reviewed |
 | SIMONETTO, T. J. A., CORDY, M., GHAMIZI, S., LE TRAON, Y., Lefebvre, C., Boystov, A., & Goujon, A. (2024). On the Impact of Industrial Delays when Mitigating Distribution Drifts: an Empirical Study on Real-world Financial Systems. In KDD Workshop on Discovering Drift Phenomena in Evolving Data Landscape. Springer. doi:10.1007/978-3-031-82346-6_4 Peer reviewed |
Cătălina Stoian, M., DYRMISHI, S., CORDY, M., Lukasiewicz, T., & Giunchiglia, E. (2024). How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data [Paper presentation]. International Conference on Learning Representations (ICLR). Peer reviewed |
 | BÜHLER, A. F., HEIN, A., RANA, L., LAZREG, S., & CORDY, M. (2024). Leveraging MBSE to support decision-making in space program design [Poster presentation]. Workshop on Model-Based Space Systems and Software Engineering (MBSE2024). Editorial reviewed |
 | JOUBAUD, D., KUBLER, S., Lourenço, R., CORDY, M., & Traon, Y. L. (2024). Decision support model for time series data augmentation method selection. IEEE Access, 1-1. doi:10.1109/access.2024.3516369 Peer Reviewed verified by ORBi |
 | DE ALENCAR CARVALHO, L. M., DEGIOVANNI, R. G., CORDY, M., Aguirre, N., Traon, Y. L., & PAPADAKIS, M. (2024). SPECBCFuzz: Fuzzing LTL Solvers with Boundary Conditions. In Proceedings - 2024 ACM/IEEE 44th International Conference on Software Engineering, ICSE 2024 (pp. 12). IEEE Computer Society. doi:10.1145/3597503.3639087 Peer reviewed |
 | SIMONETTO, T. J. A., GHAMIZI, S., & CORDY, M. (2024). TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases. In Proceedings of The Thirty-Eighth Annual Conference on Neural Information Processing Systems. TBD. Peer reviewed |
 | SIMONETTO, T. J. A., GHAMIZI, S., & CORDY, M. (2024). Towards Adaptive Attacks on Constrained Tabular Machine Learning [Paper presentation]. ICML 2024 Workshop on the Next Generation of AI Safety. Peer reviewed |
 | 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., 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 |
 | CORDY, M., LAZREG, S., Legay, A., & Schobbens, P. Y. (2023). Towards Strengthening Formal Specifications with Mutation Model Checking. In S. Chandra (Ed.), ESEC/FSE 2023 - Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Association for Computing Machinery, Inc. doi:10.1145/3611643.3613080 Peer reviewed |
 | Dimovski, A. S., LAZREG, S., CORDY, M., & Legay, A. (2023). Family-based model checking of fMultiLTL properties. In P. Arcaini (Ed.), 27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings. Association for Computing Machinery. doi:10.1145/3579027.3608976 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:https://doi.org/10.48550/arXiv.2308.01314 |
 | Cuartas, J., Aranda, J., CORDY, M., Ortiz, J., Perrouin, G., & Schobbens, P.-Y. (2023). MUPPAAL: Reducing and Removing Equivalent and Duplicate Mutants in UPPAAL. In Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023. Institute of Electrical and Electronics Engineers Inc. doi:10.1109/ICSTW58534.2023.00021 Peer reviewed |
 | GUBRI, M., CORDY, M., & LE TRAON, Y. (2023). Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/55436. |
 | Acher, M., Perrouin, G., & CORDY, M. (March 2023). BURST: Benchmarking uniform random sampling techniques. Science of Computer Programming, 226, 102914. doi:10.1016/j.scico.2022.102914 Peer Reviewed verified by ORBi |
 | 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 |
 | DYRMISHI, S., GHAMIZI, S., SIMONETTO, T. J. A., LE TRAON, Y., & CORDY, M. (2023). On the empirical effectiveness of unrealistic adversarial hardening against realistic adversarial attacks. In Conference Proceedings 2023 IEEE Symposium on Security and Privacy (SP) (pp. 1384-1400). IEEE. doi:10.1109/SP46215.2023.00049 Peer reviewed |
 | DYRMISHI, S., GHAMIZI, S., & CORDY, M. (2023). How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. 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 |
 | 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 |
 | GARG, A., DEGIOVANNI, R. G., Molina, F., CORDY, M., Aguirre, N., PAPADAKIS, M., & Traon, Y. (2023). Enabling Efficient Assertion Inference. International Symposium on Software Reliability Engineering (ISSRE), 623–634. doi:10.1109/ISSRE59848.2023.00039 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 |
 | Carvalho, L., DEGIOVANNI, R. G., Brizzio, M. A., CORDY, M., Aguirre, N., LE TRAON, Y., & PAPADAKIS, M. (2023). ACoRe: Automated Goal-Conflict Resolution. 26th International Conference on Fundamental Approaches to Software Engineering (FASE), 13991, 3–25. doi:10.1007/978-3-031-30826-0_1 Peer reviewed |
 | GHAMIZI, S., Zhang, J., CORDY, M., PAPADAKIS, M., Sugiyama, M., & LE TRAON, Y. (2023). GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. Proceedings of the International Conference on Machine Learning (ICML), 202, 11255–11282. 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 |
 | Brizzio, M. A., CORDY, M., PAPADAKIS, M., Sánchez, C. S., Aguirre, N., & DEGIOVANNI, R. G. (2023). Automated Repair of Unrealisable LTL Specifications Guided by Model Counting. Genetic and Evolutionary Computation Conference (GECCO), 1499–1507. doi:10.1145/3583131.3590454 Peer reviewed |
 | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2023). On Evaluating Adversarial Robustness of Chest X-ray Classification. Proceedings of the Workshop on Artificial Intelligence Safety 2023, 3381. 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 |
 | BERNIER, F., JIMENEZ, M., CORDY, M., & LE TRAON, Y. (2022). Faster and Cheaper Energy Demand Forecasting at Scale. In Has it Trained Yet? Workshop at the Conference on Neural Information Processing Systems. Peer reviewed |
 | Basile, D., ter Beek, M. H., LAZREG, S., CORDY, M., & Legay, A. (December 2022). Static detection of equivalent mutants in real-time model-based mutation testing: An Empirical Evaluation. Empirical Software Engineering, 27 (7). doi:10.1007/s10664-022-10149-y Peer Reviewed verified by ORBi |
 | Habchi, S., HABEN, G., SOHN, J., Franci, A., PAPADAKIS, M., CORDY, M., & LE TRAON, Y. (2022). What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness. In What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness. doi:10.1109/ICSME55016.2022.00039 Peer reviewed |
 | RANA, L., LAZREG, S., Bohlachov, V., HEIN, A., & CORDY, M. (October 2022). VARIABILITY-DRIVEN DESIGN CONFIGURATOR OF SPACE SYSTEMS TO SUPPORT DECISION-MAKERS [Paper presentation]. 10th INTERNATIONAL SYSTEMS & CONCURRENT ENGINEERING FOR SPACE APPLICATIONS CONFERENCE (SECESA 2022). |
 | GARG, A., DEGIOVANNI, R. G., JIMENEZ, M., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2022). Learning from what we know: How to perform vulnerability prediction using noisy historical data. Empirical Software Engineering. doi:10.1007/s10664-022-10197-4 Peer Reviewed verified by ORBi |
 | 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 |
 | KHANFIR, A., Koyuncu, A., PAPADAKIS, M., CORDY, M., BISSYANDE, T. F. D. A., KLEIN, J., & LE TRAON, Y. (2022). iBiR: Bug Report driven Fault Injection. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3542946 Peer Reviewed verified by ORBi |
 | HABCHI, S., HABEN, G., PAPADAKIS, M., CORDY, M., & LE TRAON, Y. (2022). A Qualitative Study on the Sources, Impacts, and Mitigation Strategies of Flaky Tests. In A Qualitative Study on the Sources, Impacts, and Mitigation Strategies of Flaky Tests. doi:10.1109/ICST53961.2022.00034 Peer reviewed |
 | LAZREG, S., Bohlachov, V., RANA, L., HEIN, A., & CORDY, M. (2022). Variability-Aware Design of Space Systems: Variability Modelling, Configuration Workflow and Research Directions. Proceedings of VAMOS 22. doi:10.1145/3510466.3510472 |
 | GHAMIZI, S., GARCIA SANTA CRUZ, B., Temple, P., CORDY, M., Perrouin, G., PAPADAKIS, M., & LE TRAON, Y. (2022). Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multi-task learning. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/50815. |
 | 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 |
 | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2022). Adversarial Robustness in Multi-Task Learning: Promises and Illusions. In Proceedings of the thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). doi:10.1609/aaai.v36i1.19950 Peer reviewed |
 | RUIZ RODRIGUEZ, M. L., KUBLER, S., de Giorgio, A., CORDY, M., Robert, J., & LE TRAON, Y. (2022). Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines. Robotics and Computer-Integrated Manufacturing. doi:10.1016/j.rcim.2022.102406 Peer reviewed |
 | SIMONETTO, T. J. A., DYRMISHI, S., GHAMIZI, S., CORDY, M., & LE TRAON, Y. (2022). A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 (pp. 1313-1319). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2022/183 Peer reviewed |
 | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2022). On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices. In The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI- 23) - SafeAI Workshop, Washington, D.C., Feb 13-14, 2023. Peer reviewed |
 | GUBRI, M., CORDY, M., PAPADAKIS, M., LE TRAON, Y., & Sen, K. (2022). Efficient and Transferable Adversarial Examples from Bayesian Neural Networks. The 38th Conference on Uncertainty in Artificial Intelligence. Peer reviewed |
 | GUBRI, M., CORDY, M., PAPADAKIS, M., Traon, Y. L., & Sen, K. (2022). LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity. In Computer Vision -- ECCV 2022 (pp. 603--618). Springer Nature Switzerland. Peer reviewed |
 | LAZREG, S., CORDY, M., & Legay, A. (2022). Verification of Variability-Intensive Stochastic Systems with Statistical Model Checking. In T. Margaria (Ed.), Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning - 11th International Symposium, ISoLA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-3-031-19759-8_27 Peer reviewed |
 | CORDY, M., RWEMALIKA, R., FRANCI, A., PAPADAKIS, M., & Harman, M. (2022). FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment. 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, 982–994. doi:10.1145/3510003.3510194 Peer reviewed |
 | FRANCI, A., CORDY, M., GUBRI, M., PAPADAKIS, M., & Traon, Y. (2022). Influence-driven data poisoning in graph-based semi-supervised classifiers. International Conference on AI Engineering: Software Engineering for AI, 77–87. doi:10.1145/3522664.3528606 Peer reviewed |
  | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2021). Evasion Attack STeganography: Turning Vulnerability Of Machine Learning ToAdversarial Attacks Into A Real-world Application. Proceedings of International Conference on Computer Vision 2021. doi:10.1109/ICCVW54120.2021.00010 Peer reviewed |
 | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2021). Requirements And Threat Models of Adversarial Attacks and Robustness of Chest X-ray classification. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/48411. |
 | HABEN, G., HABCHI, S., PAPADAKIS, M., CORDY, M., & LE TRAON, Y. (2021). A Replication Study on the Usability of Code Vocabulary in Predicting Flaky Tests. In 18th International Conference on Mining Software Repositories. doi:10.1109/MSR52588.2021.00034 Peer reviewed |
 | Castro, T., Teixeira, L., Alves, V., Apel, S., CORDY, M., & Gheyi, R. (23 April 2021). A Formal Framework of Software Product Line Analyses. ACM Transactions on Software Engineering and Methodology, 30 (3), 1-37. doi:10.1145/3442389 Peer Reviewed verified by ORBi |
 | Acher, M., Perrouin, G., & CORDY, M. (2021). BURST: a benchmarking platform for uniform random sampling techniques. In SPLC '21: 25th ACM International Systems and Software Product Line Conference, Leicester, United Kindom, September 6-11, 2021, Volume B (pp. 36--40). ACM. doi:10.1145/3461002.3473070 Peer reviewed |
 | CORDY, M., LAZREG, S., PAPADAKIS, M., & Legay, A. (2021). Statistical model checking for variability-intensive systems: applications to bug detection and minimization. Formal Aspects of Computing, 33 (6), 1147--1172. doi:10.1007/s00165-021-00563-2 Peer Reviewed verified by ORBi |
 | MA, W., PAPADAKIS, M., Tsakmalis, A., CORDY, M., & LE TRAON, Y. (2021). Test Selection for Deep Learning Systems. ACM Transactions on Software Engineering and Methodology, 30 (2), 13:1--13:22. doi:10.1145/3417330 Peer Reviewed verified by ORBi |
 | TITCHEU CHEKAM, T., PAPADAKIS, M., CORDY, M., & LE TRAON, Y. (2021). Killing Stubborn Mutants with Symbolic Execution. ACM Transactions on Software Engineering and Methodology, 30 (2), 19:1--19:23. doi:10.1145/3425497 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 |
 | MOULINE, L., CORDY, M., & LE TRAON, Y. (2020). Load approximation for uncertain topologies in the low-voltage grid. In INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS, 11-13 November 2020 (pp. 1-6). doi:10.1109/SmartGridComm47815.2020.9302940 Peer reviewed |
 | Basile, D., Ter Beek, M., CORDY, M., & Legay, A. (2020). Tackling the equivalent mutant problem in real-time systems: the 12 commandments of model-based mutation testing. In SOFTWARE PRODUCT LINE CONFERENCE. doi:10.1145/3382025.3414966 Peer reviewed |
 | GHAMIZI, S., RWEMALIKA, R., CORDY, M., VEIBER, L., BISSYANDE, T. F. D. A., PAPADAKIS, M., KLEIN, J., & LE TRAON, Y. (2020). Data-driven simulation and optimization for covid-19 exit strategies. In S. GHAMIZI, R. RWEMALIKA, M. CORDY, L. VEIBER, T. F. D. A. BISSYANDE, M. PAPADAKIS, J. KLEIN, ... Y. LE TRAON, Data-driven simulation and optimization for covid-19 exit strategies (pp. 3434-3442). New York, NY, United States: Association for Computing Machinery. doi:10.1145/3394486.3412863 Peer reviewed |
 | CORDY, M., PAPADAKIS, M., & Legay, A. (2020). Statistical Model Checking for Variability-Intensive Systems. In FUNDAMENTAL APPROACHES TO SOFTWARE ENGINEERING, Dublin 22-25 April 2020. Peer reviewed |
 | ANTONIADIS, N., CORDY, M., Sifaleras, A., & LE TRAON, Y. (2020). Preventing Overloading Incidents on Smart Grids: A Multiobjective Combinatorial Optimization Approach. In Communications in Computer and Information Science (pp. 269-281). Springer, Cham. doi:10.1007/978-3-030-41913-4_22 Peer reviewed |
 | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2020). Adversarial Embedding: A robust and elusive Steganography and Watermarking technique [Paper presentation]. IEEE Symposium on Security and Privacy. |
 | GHAMIZI, S., RWEMALIKA, R., CORDY, M., LE TRAON, Y., & PAPADAKIS, M. (2020). Pandemic Simulation and Forecasting of exit strategies:Convergence of Machine Learning and EpidemiologicalModels. University of Luxembourg. https://orbilu.uni.lu/handle/10993/43166 |
 | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2020). FeatureNET: Diversity-driven Generation of Deep Learning Models. In International Conference on Software Engineering (ICSE). doi:10.1145/3377812.3382153 Peer reviewed |
 | GHAMIZI, S., CORDY, M., GUBRI, M., PAPADAKIS, M., Boystov, A., LE TRAON, Y., & Goujon, A. (2020). Search-based adversarial testing and improvement of constrained credit scoring systems. In ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE '20), November 8-13, 2020. Peer reviewed |
 | CORDY, M., & Legay, A. (December 2019). Verification and abstraction of real-time variability-intensive systems. International Journal on Software Tools for Technology Transfer, 21 (6), 635-649. doi:10.1007/s10009-019-00537-z Peer Reviewed verified by ORBi |
 | CORDY, M., & Lazreg, S. (2019). Automated evaluation of embedded-system design alternatives. In Proceedings of the 23rd International Systems and Software Product Line Conference, SPLC 2019, Volume A, Paris, France, September 9-13, 2019 (pp. 52:1). Peer reviewed |
 | Patout, P.-A., & CORDY, M. (2019). Towards context-aware automated writing evaluation systems. In Proceedings of the 1st ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, EASEAI@ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 26, 2019 (pp. 17-20). Peer reviewed |
 | Lazreg, S., CORDY, M., Collet, P., Heymans, P., & Mosser, S. (2019). Multifaceted automated analyses for variability-intensive embedded systems. In Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019 (pp. 854-865). Peer reviewed |
 | CORDY, M., Legay, A., Lazreg, S., & Collet, P. (2019). Towards sampling and simulation-based analysis of featured weighted automata. In Proceedings of the 7th International Workshop on Formal Methods in Software Engineering (pp. 61-64). doi:10.1109/FormaliSE.2019.00015 Peer reviewed |
 | Amand, B., CORDY, M., Heymans, P., Acher, M., Temple, P., & Jézéquel, J.-M. (2019). Towards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction. In Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019, Leuven, Belgium, February 06-08, 2019 (pp. 7:1-7:9). doi:10.1145/3302333.3302338 Peer reviewed |
Arcelli, F., Walter, B., Ampatzoglou, A., Palomba, F., Perrouin, G., Acher, M., CORDY, M., & Devroey, X. (Eds.). (2019). Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation. ACM. |
 | CORDY, M., Devroey, X., Legay, A., Perrouin, G., Classen, A., Heymans, P., Schobbens, P.-Y., & Raskin, J.-F. (2019). A Decade of Featured Transition Systems. In From Software Engineering to Formal Methods and Tools, and Back - Essays Dedicated to Stefania Gnesi on the Occasion of Her 65th Birthday (pp. 285-312). doi:10.1007/978-3-319-51963-0_35 Peer reviewed |
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 | GHAMIZI, S., CORDY, M., PAPADAKIS, M., & LE TRAON, Y. (2019). Automated Search for Configurations of Deep Neural Network Architectures. In Automated Search for Configurations of Convolutional Neural Network Architectures. doi:10.1145/3336294.3336306 Peer reviewed |
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 | JIMENEZ, M., TITCHEU CHEKAM, T., CORDY, M., PAPADAKIS, M., KINTIS, M., LE TRAON, Y., & Harman, M. (2018). Are mutants really natural? A study on how “naturalness” helps mutant selection. Proceedings of 12th International Symposium on
Empirical Software Engineering and Measurement (ESEM'18). doi:10.1145/3239235.3240500 Peer reviewed |
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 | JIMENEZ, M., CORDY, M., LE TRAON, Y., & PAPADAKIS, M. (September 2018). On the impact of tokenizer and parameters on N-gram based Code Analysis [Paper presentation]. 34th IEEE International Conference on Software Maintenance and Evolution (ICSME'18), Madrid, Spain. |
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 | JIMENEZ, M., CORDY, M., KINTIS, M., TITCHEU CHEKAM, T., LE TRAON, Y., & PAPADAKIS, M. (2017). On the Naturalness of Mutants. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/35014. |
