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Dakhel, A. M., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., & Jiang, Z. M. (.J.K. ). (2023). GitHub Copilot AI pair programmer: Asset or Liability? Journal of Systems and Software, 203, 111734 (23 pages). External link
Jamshidi, S., Nikanjam, A., Hamdaqa, M. A., & Khomh, F. (2023). Attack Detection by Using Deep Learning for Cyber-Physical System. In Artificial Intelligence for Cyber-Physical Systems Hardening (155-179). External link
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. External link
Mahdavimoghadam, M., Nikanjam, A., & Abdoos, M. (2022). Improved reinforcement learning in cooperative multi-agent environments using knowledge transfer. Journal of Supercomputing, 25 pages. External link
Mindom, P. S. N., Nikanjam, A., Khomh, F., & Mullins, J. (2021, December). On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods [Paper]. 21st International Conference on Software Quality, Reliability and Security (QRS 2021), Hainan, China. External link
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), 14 (27 pages). External link
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). External link
Openja, M., Nikanjam, A., Yahmed, A. H., Khomh, F., & Jiang, Z. M. J. (2022, October). An Empirical Study of Challenges in Converting Deep Learning Models [Paper]. 39th IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Roy, S., Laberge, G., Roy, B., Khomh, F., Nikanjam, A., & Mondal, S. (2022, October). Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Rivera-Landos, E., Khomh, F., & Nikanjam, A. (2021, December). The Challenge of Reproducible ML: An Empirical Study on The Impact of Bugs [Paper]. 21st International Conference on Software Quality, Reliability and Security (QRS 2021), Hainan, China. External link
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, 12 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., 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
