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Morovati, M. M., Nikanjam, A., Tambon, F., Khomh, F., & Jiang, Z. M. (2024). Bug characterization in machine learning-based systems. Empirical Software Engineering, 29(1), 14 (29 pages). Lien externe
Morovati, M. M., Tambon, F., Taraghi, M., Nikanjam, A., & Khomh, F. (2024). Common challenges of deep reinforcement learning applications development: an empirical study. Empirical Software Engineering, 29, 95 (33 pages). Lien externe
Tambon, F., Khomh, F., & Antoniol, G. (2024). GIST : Generated Inputs Sets Transferability in Deep Learning. ACM Transactions on Software Engineering and Methodology, 33(8), 214 (38 pages). Lien externe
Tambon, F., Nikanjam, A., An, L., Khomh, F., & Antoniol, G. (2024). Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow. Empirical Software Engineering, 29(1), 10 (34 pages). Lien externe
Kouemo Ngassom, S., Moradidakhel, A., Tambon, F., & Khomh, F. (juillet 2024). Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs [Communication écrite]. 1st ACM International Conference on AI-Powered Software (ALWARE 2024), Porto de Galinhas, Brazil. Lien externe
Mahu, A.-M., Singh, A., Tambon, F., Ouellette, B., Delisle, J.-F., Paul, T., Khomh, F., Marois, A., & Doyon-Poulin, P. (juin 2024). Validation of vigilance decline capability in a simulated test environment: a preliminary step towards neuroadaptive control [Communication écrite]. 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024), Nice, France. Disponible
Taraghi, M., Dorcelus, G., Foundjem, A. T., Tambon, F., & Khomh, F. (mars 2024). Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends [Communication écrite]. 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024), Rovaniemi, Finland. Lien externe
Tambon, F. (2024). Who Tests the Testers? Assessing the Effectiveness and Trustworthiness of Deep Learning Model Testing Techniques [Thèse de doctorat, Polytechnique Montréal]. Disponible
Tambon, F. (2024). GIST: Generated Inputs Sets Transferability in Deep Learning (Part 2) [Ensemble de données]. Lien externe
