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ORCIDHoussem Ben Braiek
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2020 – today
- 2024
[i14]Houssem Ben Braiek, Foutse Khomh:
Machine Learning Robustness: A Primer. CoRR abs/2404.00897 (2024)- [i13]Altaf Allah Abbassi, Houssem Ben Braiek, Foutse Khomh, Thomas Reid:
Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems. CoRR abs/2409.09108 (2024) - [i12]Dmytro Humeniuk, Houssem Ben Braiek, Thomas Reid, Foutse Khomh:
In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators. CoRR abs/2410.19277 (2024) - 2023
- [j8]Rached Bouchoucha
, Houssem Ben Braiek, Foutse Khomh, Sonia Bouzidi, Rania Zaatour:
Robustness assessment of hyperspectral image CNNs using metamorphic testing. Inf. Softw. Technol. 162: 107281 (2023) - [j7]Houssem Ben Braiek, Foutse Khomh:
Testing Feedforward Neural Networks Training Programs. ACM Trans. Softw. Eng. Methodol. 32(4): 105:1-105:61 (2023) - [j6]Houssem Ben Braiek, Thomas Reid, Foutse Khomh:
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation. IEEE Trans. Reliab. 72(3): 1161-1175 (2023) - [c6]Ahmed Haj Yahmed, Rached Bouchoucha, Houssem Ben Braiek, Foutse Khomh:
An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems. ASE 2023: 1314-1325 - [i11]Ahmed Haj Yahmed, Rached Bouchoucha, Houssem Ben Braiek, Foutse Khomh:
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems. CoRR abs/2308.12445 (2023) - 2022
- [j5]Amin Nikanjam, Mohammad Mehdi Morovati, Foutse Khomh, Houssem Ben Braiek:
Faults in deep reinforcement learning programs: a taxonomy and a detection approach. Autom. Softw. Eng. 29(1): 8 (2022) - [j4]Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse Khomh, Sonia Bouzidi, Rania Zaatour:
DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment. Empir. Softw. Eng. 27(7): 193 (2022) - [j3]Amin Nikanjam, Houssem Ben Braiek, Mohammad Mehdi Morovati, Foutse Khomh:
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations. ACM Trans. Softw. Eng. Methodol. 31(1): 14:1-14:27 (2022) - [c5]Houssem Ben Braiek, Ali Tfaily, Foutse Khomh, Thomas Reid, Ciro Guida:
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design. ASE 2022: 94:1-94:13 - [i10]Houssem Ben Braiek, Foutse Khomh:
Testing Feedforward Neural Networks Training Programs. CoRR abs/2204.00694 (2022) - [i9]Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse Khomh, Sonia Bouzidi, Rania Zaatour:
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment. CoRR abs/2207.06282 (2022) - [i8]Houssem Ben Braiek, Thomas Reid, Foutse Khomh:
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation. CoRR abs/2209.03431 (2022) - [i7]Houssem Ben Braiek, Ali Tfaily, Foutse Khomh, Thomas Reid, Ciro Guida:
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design. CoRR abs/2209.03438 (2022) - 2021
- [i6]Amin Nikanjam, Mohammad Mehdi Morovati, Foutse Khomh, Houssem Ben Braiek:
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach. CoRR abs/2101.00135 (2021) - [i5]Amin Nikanjam, Houssem Ben Braiek, Mohammad Mehdi Morovati, Foutse Khomh:
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations. CoRR abs/2105.08095 (2021) - [i4]Ettore Merlo, Mira Marhaba, Foutse Khomh, Houssem Ben Braiek, Giuliano Antoniol:
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases. CoRR abs/2107.13491 (2021) - 2020
- [j2]Houssem Ben Braiek, Foutse Khomh:
On testing machine learning programs. J. Syst. Softw. 164: 110542 (2020) - [c4]Hadhemi Jebnoun, Houssem Ben Braiek, Mohammad Masudur Rahman, Foutse Khomh:
The Scent of Deep Learning Code: An Empirical Study. MSR 2020: 420-430
2010 – 2019
- 2019
- [c3]Houssem Ben Braiek, Foutse Khomh:
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks. ICSME 2019: 454-458 - [c2]Houssem Ben Braiek, Foutse Khomh:
TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs. QRS 2019: 426-433 - [i3]Houssem Ben Braiek, Foutse Khomh:
TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs. CoRR abs/1909.02562 (2019) - [i2]Houssem Ben Braiek, Foutse Khomh:
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks. CoRR abs/1909.02563 (2019) - 2018
- [j1]Sonia Bouzidi, Houssem Ben Braiek:
Improved algorithm for hyperspectral image classification. J. Electronic Imaging 27(06): 063032 (2018) - [c1]Houssem Ben Braiek, Foutse Khomh, Bram Adams:
The open-closed principle of modern machine learning frameworks. MSR 2018: 353-363 - [i1]Houssem Ben Braiek, Foutse Khomh:
On Testing Machine Learning Programs. CoRR abs/1812.02257 (2018)
Coauthor Index
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