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Yahmed, A. H., Bouchoucha, R., Ben Braiek, H., & Khomh, F. (septembre 2023). An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems [Communication écrite]. 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023), Echternach, Luxembourg. Lien externe
Ben Braiek, H., & Khomh, F. (2023). Testing Feedforward Neural Networks Training Programs. ACM Transactions on Software Engineering and Methodology, 32(4), 1-61. Lien externe
Ben Braiek, H. (2022). Debugging and Testing Deep Learning Software Systems [Thèse de doctorat, Polytechnique Montréal]. Disponible
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
Yahmed, A. H., Ben Braiek, H., Khomh, F., Bouzidi, S., & Zaatour, R. (2022). DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment. Empirical Software Engineering, 27(7), 193 (32 pages). 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
Ben Braiek, H., Reid, T., & Khomh, F. (2022). Physics-guided adversarial machine learning for aircraft systems simulation. IEEE Transactions on Reliability, 72(3), 1161-1175. Lien externe
Ben Braiek, H., Tfaily, A., Khomh, F., Reid, T., & Guida, C. (octobre 2022). SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design [Communication écrite]. 37th IEEE/ACM International Conference on Automated Software Engineering (ASE 2022), Rochester, MI, USA (13 pages). Lien externe
Jebnoun, H., Ben Braiek, H., Rahman, M. M., & Khomh, F. (juin 2020). The scent of deep learning code : an empirical study [Communication écrite]. 17th International Conference on Mining Software Repositories (MSR 2020), Seoul, Republic of Korea. Lien externe
Ben Braiek, H., & Khomh, F. (2020). On testing machine learning programs. Journal of Systems and Software, 164, 110542 (18 pages). Lien externe
Ben Braiek, H. (2019). Towards Debugging and Testing Deep Learning Systems [Mémoire de maîtrise, Polytechnique Montréal]. Disponible
Ben Braiek, H., & Khomh, F. (septembre 2019). DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2019), Cleveland, OH, United states. Lien externe
Ben Braiek, H., & Khomh, F. (juillet 2019). TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs [Communication écrite]. 19th IEEE International Conference on Software Quality, Reliability and Security (QRS 2019), Sofia, Bulgaria. Lien externe
Ben Braiek, H., Khomh, F., & Adams, B. (mai 2018). The open-closed principle of modern machine learning frameworks [Communication écrite]. 15th International Conference on Mining Software Repositories (MSR 2018), Gothenburg, Sweden. Lien externe
