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Kouemo Ngassom, S., Moradi Dakhel, A., Tambon, F., & Khomh, F. (2024, July). Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs [Paper]. 1st ACM International Conference on AI-Powered Software (ALWARE 2024), Porto de Galinhas, Brazil. External link
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). External link
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). External link
Taraghi, M., Dorcelus, G., Foundjem, A., Tambon, F., & Khomh, F. (2024, March). Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends [Paper]. 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024), Rovaniemi, Finland. External link
Tambon, F. (2024). GIST: Generated Inputs Sets Transferability in Deep Learning (Part 2) [Dataset]. External link
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). External link
Tambon, F., Majfinasab, V., Nikanjam, A., Khomh, F., & Antoniol, G. (2023, April). Mutation testing of deep reinforcement learning based on real faults [Paper]. 16th IEEE Conference on Software Testing, Verification and Validation (ICST 2023), Dublin, Ireland. External link
Tambon, F., Khomh, F., & Antoniol, G. (2023). A probabilistic framework for mutation testing in deep neural networks. Information and Software Technology, 155, 107129 (13 pages). External link
Tambon, F., Laberge, G., An, L., Nikanjam, A., Mindom, P. S. N., Pequignot, Y., Khomh, F., Antoniol, G., Merlo, E., & Laviolette, F. (2022). How to certify machine learning based safety-critical systems? A systematic literature review. Automated Software Engineering, 29(2). External link
Tambon, F., Khomh, F., & Antoniol, G. (2022). A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1 [Dataset]. External link
Tambon, F., Khomh, F., & Antoniol, G. (2022). A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2 [Dataset]. External link
Tambon, F., Khomh, F., & Antoniol, G. (2022). A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3 [Dataset]. External link