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Ben Braiek, H. (2022). Debugging and Testing Deep Learning Software Systems [Thèse de doctorat, Polytechnique Montréal]. Disponible
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
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
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
