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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
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
Jamshidi, S., Nikanjam, A., Hamdaqa, M. A., & Khomh, F. (2023). Attack Detection by Using Deep Learning for Cyber-Physical System. Dans Artificial Intelligence for Cyber-Physical Systems Hardening (Vol. 2, 155-179). Lien externe
Morovati, M. M., Nikanjam, A., Khomh, F., & Jiang, Z. M. (2023). Bugs in machine learning-based systems: a faultload benchmark. Empirical Software Engineering, 28(3), 33 pages. Lien externe
Nouwou Mindom, P. S., Nikanjam, A., & Khomh, F. (2023). A comparison of reinforcement learning frameworks for software testing tasks. Empirical Software Engineering, 28(5), 111 (76 pages). Lien externe
Yahmed, A. H., Allah Abbassi, A., Nikanjam, A., Li, H., & Khomh, F. (octobre 2023). Deploying deep reinforcement learning systems: a taxonomy of challenges [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2023), Bogota, Colombia. Lien externe
Dakhel, A. M., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., & Jiang, Z. M. (2023). GitHub Copilot AI pair programmer: Asset or Liability? Journal of Systems and Software, 203, 111734 (23 pages). Lien externe
Tambon, F., Majfinasab, V., Nikanjam, A., Khomh, F., & Antoniol, G. (avril 2023). Mutation testing of deep reinforcement learning based on real faults [Communication écrite]. 16th IEEE Conference on Software Testing, Verification and Validation (ICST 2023), Dublin, Ireland. Lien externe
Nikanjam, A., Ben Braiek, H., Morovati, M. M., & Khomh, F. (2022). Automatic Fault Detection for Deep Learning Programs Using Graph Transformations. ACM Transactions on Software Engineering and Methodology, 31(1), 1-27. Lien externe
Openja, M., Nikanjam, A., Yahmed, A. H., Khomh, F., & Jiang, Z. M. J. (octobre 2022). An Empirical Study of Challenges in Converting Deep Learning Models [Communication écrite]. 39th IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Nikanjam, A., Morovati, M. M., Khomh, F., & Ben Braiek, H. (2022). Faults in deep reinforcement learning programs: a taxonomy and a detection approach. Automated Software Engineering, 29(1), 8 (32 pages). Lien externe
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). Lien externe
Mahdavimoghadam, M., Nikanjam, A., & Abdoos, M. (2022). Improved reinforcement learning in cooperative multi-agent environments using knowledge transfer. Journal of Supercomputing, 78(8), 10455-10479. Lien externe
Shajoonnezhad, N., & Nikanjam, A. (2022). A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data. International Journal of Machine Learning and Cybernetics, 14(3), 947-958. Lien externe
Roy, S., Laberge, G., Roy, B., Khomh, F., Nikanjam, A., & Mondal, S. (octobre 2022). Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Rivera-Landos, E., Khomh, F., & Nikanjam, A. (décembre 2021). The Challenge of Reproducible ML: An Empirical Study on The Impact of Bugs [Communication écrite]. 21st International Conference on Software Quality, Reliability and Security (QRS 2021), Hainan, China. Lien externe
Mindom, P. S. N., Nikanjam, A., Khomh, F., & Mullins, J. (décembre 2021). On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods [Communication écrite]. 21st International Conference on Software Quality, Reliability and Security (QRS 2021), Hainan, China. Lien externe
