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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Ben Braiek, H., & Khomh, F. (2025). Machine learning robustness: a primer. In Lorenzi, M., & Zuluaga, M. A. (eds.), Trustworthy AI in Medical Imaging (pp. 37-71). External link
Ben Braiek, H., & Khomh, F. (2023). Testing Feedforward Neural Networks Training Programs. ACM Transactions on Software Engineering and Methodology, 32(4), 1-61. External link
Ben Braiek, H. (2022). Debugging and Testing Deep Learning Software Systems [Ph.D. thesis, Polytechnique Montréal]. Available
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. External link
Ben Braiek, H., Tfaily, A., Khomh, F., Reid, T., & Guida, C. (2022, October). SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design [Paper]. 37th IEEE/ACM International Conference on Automated Software Engineering (ASE 2022), Rochester, MI, USA (13 pages). External link
Ben Braiek, H., & Khomh, F. (2020). On testing machine learning programs. Journal of Systems and Software, 164, 110542 (18 pages). External link
Ben Braiek, H. (2019). Towards Debugging and Testing Deep Learning Systems [Master's thesis, Polytechnique Montréal]. Available
Ben Braiek, H., & Khomh, F. (2019, September). DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2019), Cleveland, OH, United states. External link
Ben Braiek, H., & Khomh, F. (2019, July). TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs [Paper]. 19th IEEE International Conference on Software Quality, Reliability and Security (QRS 2019), Sofia, Bulgaria. External link
Ben Braiek, H., Khomh, F., & Adams, B. (2018, May). The open-closed principle of modern machine learning frameworks [Paper]. 15th International Conference on Mining Software Repositories (MSR 2018), Gothenburg, Sweden. External link
Humeniuk, D., Ben Braiek, H., Reid, T., & Khomh, F. (2024, October). In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators [Paper]. 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024), Sacramento, CA, USA. External link
Jebnoun, H., Ben Braiek, H., Rahman, M. M., & Khomh, F. (2020, June). The scent of deep learning code : an empirical study [Paper]. 17th International Conference on Mining Software Repositories (MSR 2020), Seoul, Republic of Korea. 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), 1-27. 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
Yahmed, A. H., Bouchoucha, R., Ben Braiek, H., & Khomh, F. (2023, September). An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems [Paper]. 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023), Echternach, Luxembourg. External link
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). External link
