Publications

2024

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. arXiv preprint arXiv:2405.13932. 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