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Bouchoucha, R., Haj Yahmed, A., Patil, D., Rajendran, J., Nikanjam, A., Anbil Parthipan, S. C., & Khomh, F. (octobre 2024). Toward Debugging Deep Reinforcement Learning Programs with RLExplorer [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2024), Flagstaff, AZ, USA. 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
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
Islam, M. R., Roy, B., Hassan, M., & Nikanjam, A. (novembre 2024). Just-in-Time and Real-Time Bug-Inducing Commit Prediction Using a Federated Learning Approach [Communication écrite]. 34th International Conference on Collaborative Advances in Software and COmputiNg (CASCON 2024), Toronto, ON, Canada. 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
Majdinasab, V., Nikanjam, A., & Khomh, F. (2024). Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code. ACM Transactions on Software Engineering and Methodology. Lien externe
Mindom, P. S. N., Nikanjam, A., & Khomh, F. (2024). Harnessing pre-trained generalist agents for software engineering tasks. Empirical Software Engineering, 30(1). Lien externe
Moradidakhel, A., 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
Moradidakhel, A., 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
Moradidakhel, A., 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
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
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
