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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
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). Lien externe
Côté, P.-O., Nikanjam, A., Ahmed, N., Humeniuk, D., & Khomh, F. (2024). Data cleaning and machine learning: a systematic literature review. Automated Software Engineering, 31(2), 54 (75 pages). Lien externe
Dakhel, A. M., Nikanjam, A., Majdinasab, V., Khomh, F., & Desmarais, M. C. (2024). Effective test generation using pre-trained Large Language Models and mutation testing. Information and Software Technology, 171, 107468 (17 pages). Lien externe
Côté, P.-O., Nikanjam, A., Bouchoucha, R., Basta, I., Abidi, M., & Khomh, F. (2024). Quality issues in machine learning software systems. Empirical Software Engineering, 29(6), 149 (47 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
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
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
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
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
Jamshidi, S., Amirnia, A., Nikanjam, A., & Khomh, F. (avril 2024). Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning [Communication écrite]. 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2024) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40 2024), Hasselt, Belgium. Publié dans Procedia Computer Science, 238. 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
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
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
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
Morovati, M. M., Nikanjam, A., Khomh, F., & Jiang, Z. M. (2023). Bugs in machine learning-based systems: a faultload benchmark [Ensemble de données]. Lien externe
Dakhel, A. M., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024). Generative AI for Software Development: A Family of Studies on Code Generation. Dans Generative AI for Effective Software Development (p. 151-172). Lien externe
Dakhel, A. M., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024). An Overview on Large Language Models. Dans Generative AI for Effective Software Development (p. 3-21). 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, p. 155-179). Lien externe
