172 citations · 802 across the 171 of their papers we have counts for
24 papers · 1 filter
Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures
Saeid Jamshidi, Foutse Khomh, Carol Fung +2
Time integrity across distributed Internet of Things (IoT) devices is fundamental to reliable sensing, control, and security in energy cyber-physical systems. However, operational…
FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
Moses Openja, Paolo Arcaini, Foutse Khomh +1
Deep neural networks (DNNs) are being utilized in various aspects of our daily lives, including high-stakes decision-making applications that impact individuals. However, these sys…
Towards Assessing Deep Learning Test Input Generators
Seif Mzoughi, Ahmed Haj yahmed, Mohamed Elshafei +2
Deep Learning (DL) systems are increasingly deployed in safety-critical applications, yet they remain vulnerable to robustness issues that can lead to significant failures. While n…
Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
Altaf Allah Abbassi, Houssem Ben Braiek, Foutse Khomh +1
The industry increasingly relies on deep learning (DL) technology for manufacturing inspections, which are challenging to automate with rule-based machine vision algorithms. DL-pow…
Machine Learning Robustness: A Primer
Houssem Ben Braiek, Foutse Khomh
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems.…
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon, Foutse Khomh, Giuliano Antoniol
To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods for test case generation techniques are being developed. When confronted…
Detection and Evaluation of bias-inducing Features in Machine learning
Moses Openja, Gabriel Laberge, Foutse Khomh
The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This impli…
Data Cleaning and Machine Learning: A Systematic Literature Review
Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed +2
Context: Machine Learning (ML) is integrated into a growing number of systems for various applications. Because the performance of an ML model is highly dependent on the quality of…
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems
Ahmed Haj Yahmed, Rached Bouchoucha, Houssem Ben Braiek +1
Deep reinforcement learning (DRL) is increasingly applied in large-scale productions like Netflix and Facebook. As with most data-driven systems, DRL systems can exhibit undesirabl…
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam +2
Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement learning, has shown significant potential in achieving human-level autonomy in a wide range of dom…
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
Florian Tambon, Vahid Majdinasab, Amin Nikanjam +2
Testing Deep Learning (DL) systems is a complex task as they do not behave like traditional systems would, notably because of their stochastic nature. Nonetheless, being able to ad…
Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?
Khaled Badran, Pierre-Olivier Côté, Amanda Kolopanis +5
As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems. Several fairness pre-…
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design
Houssem Ben Braiek, Ali Tfaily, Foutse Khomh +2
Aircraft industry is constantly striving for more efficient design optimization methods in terms of human efforts, computation time, and resource consumption. Hybrid surrogate opti…
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation
Houssem Ben Braiek, Thomas Reid, Foutse Khomh
In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowled…
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse Khomh +2
Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone. This is owing to…
An Empirical Study of Challenges in Converting Deep Learning Models
Moses Openja, Amin Nikanjam, Ahmed Haj Yahmed +3
There is an increase in deploying Deep Learning (DL)-based software systems in real-world applications. Usually DL models are developed and trained using DL frameworks that have th…
Fool SHAP with Stealthily Biased Sampling
Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara +2
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies h…
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods
Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh +1
The increasing adoption of Reinforcement Learning in safety-critical systems domains such as autonomous vehicles, health, and aviation raises the need for ensuring their safety. Ex…
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
Gabriel Laberge, Yann Pequignot, Alexandre Mathieu +2
Post-hoc global/local feature attribution methods are progressively being employed to understand the decisions of complex machine learning models. Yet, because of limited amounts o…
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases
Ettore Merlo, Mira Marhaba, Foutse Khomh +2
Neural network test cases are meant to exercise different reasoning paths in an architecture and used to validate the prediction outcomes. In this paper, we introduce "computationa…
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Florian Tambon, Gabriel Laberge, Le An +7
Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aer…
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks
Florian Tambon, Giulio Antoniol, Foutse Khomh
Deep Neural Networks (DNN) applications are increasingly becoming a part of our everyday life, from medical applications to autonomous cars. Traditional validation of DNN relies on…
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks
Houssem Ben Braiek, Foutse khomh
The increasing inclusion of Deep Learning (DL) models in safety-critical systems such as autonomous vehicles have led to the development of multiple model-based DL testing techniqu…
TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs
Houssem Ben Braiek, Foutse Khomh
The increasing inclusion of Machine Learning (ML) models in safety critical systems like autonomous cars have led to the development of multiple model-based ML testing techniques.…