Publications
2025
Tambon, F., Moradi-Dakhel, A., Nikanjam, A., Khomh, F., Desmarais, M., Antoniol, G. (2025). Bugs in Large Language Models Generated Code: An Empirical Study. arXiv preprint arXiv:2403.08937 Preprint
2024
Taraghi, M., Dorcelus, G., Foundjem, A., Tambon, F., Khomh, F. (2024) Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends. 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). Paper, Preprint
Mahu, A., Singh, A., Tambon, F., Ouellette, B., Delisle, J. F., Paul, T. S., … & Doyon-Poulin, P. (2024). Validation of Vigilance Decline Capability in a Simulated Test Environment: A Preliminary Step Towards Neuroadaptive Control. Neuroergonomics and Cognitive Engineering, 45. Paper (Open Access)
Ngassom, S. K., Dakhel, A. M., Tambon, F., & Khomh, F. (2024). Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs. AIware 2024: Proceedings of the 1st ACM International Conference on AI-Powered Software Pages 122 - 130. Paper,Preprint
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(4), 95. Paper, Preprint
Morovati, M.M., Nikanjam, A., Tambon, F. et al. Bug characterization in machine learning-based systems. Empir Software Eng 29, 14. Paper, Preprint
Tambon, F., Nikanjam, A., An, L., Khomh, F. and Antoniol, G., “Silent bugs in deep learning frameworks: An empirical study of Keras and TensorFlow”. Empirical Software Engineering 29 (1), 10. Paper, Preprint
2023
Tambon, F., Khomh, F., & Antoniol, G. (2023). GIST: Generated Inputs Sets Transferability in Deep Learning. ACM Transactions on Software Engineering and Methodology. Paper, Preprint
Tambon, F., Majdinasab, V., Nikanjam, A., Khomh, F., and Antoniol, G., “Mutation Testing of Deep Reinforcement Learning Based on Real Faults,” 2023 IEEE Conference on Software Testing, Verification and Validation (ICST), Dublin, Ireland, 2023, pp. 188-198. Paper, Preprint
Tambon, F., Khomh, F., Antoniol, A. “A probabilistic framework for mutation testing in deep neural networks.” Information and Software Technology 155, 107129. Paper, Preprint
2022
Tambon, F., Laberge, G., An, L. et al. “How to certify machine learning based safety-critical systems? A systematic literature review.” Autom Softw Eng 29, 38 (2022). Paper, Preprint