Houssem Ben Braiek

Orcid: 0000-0003-0542-9140

According to our database1, Houssem Ben Braiek authored at least 15 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation.
IEEE Trans. Reliab., September, 2023

Testing Feedforward Neural Networks Training Programs.
ACM Trans. Softw. Eng. Methodol., July, 2023

Robustness assessment of hyperspectral image CNNs using metamorphic testing.
Inf. Softw. Technol., 2023

An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems.
Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

2022
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations.
ACM Trans. Softw. Eng. Methodol., 2022

DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment.
Empir. Softw. Eng., 2022

Faults in deep reinforcement learning programs: a taxonomy and a detection approach.
Autom. Softw. Eng., 2022

SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design.
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022

2021
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases.
CoRR, 2021

2020
On testing machine learning programs.
J. Syst. Softw., 2020

The Scent of Deep Learning Code: An Empirical Study.
Proceedings of the MSR '20: 17th International Conference on Mining Software Repositories, 2020

2019
TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs.
Proceedings of the 19th IEEE International Conference on Software Quality, 2019

DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks.
Proceedings of the 2019 IEEE International Conference on Software Maintenance and Evolution, 2019

2018
Improved algorithm for hyperspectral image classification.
J. Electronic Imaging, 2018

The open-closed principle of modern machine learning frameworks.
Proceedings of the 15th International Conference on Mining Software Repositories, 2018


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