172 citations · 802 across the 171 of their papers we have counts for
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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-…
Studying the Characteristics of AIOps Projects on GitHub
Roozbeh Aghili, Heng Li, Foutse Khomh
Artificial Intelligence for IT Operations (AIOps) leverages AI approaches to handle the massive amount of data generated during the operations of software systems. Prior works have…
An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks
Mohamed Raed El aoun, Lionel Nganyewou Tidjon, Ben Rombaut +2
Recent advances in deep learning (dl) have led to the release of several dl software libraries such as pytorch, Caffe, and TensorFlow, in order to assist machine learning (ml) prac…
Reliable Malware Analysis and Detection using Topology Data Analysis
Lionel Nganyewou Tidjon, Foutse Khomh
Increasingly, malwares are becoming complex and they are spreading on networks targeting different infrastructures and personal-end devices to collect, modify, and destroy victim i…
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…
An Empirical Study on the Usage of Automated Machine Learning Tools
Forough Majidi, Moses Openja, Foutse Khomh +1
The popularity of automated machine learning (AutoML) tools in different domains has increased over the past few years. Machine learning (ML) practitioners use AutoML tools to auto…
Quality issues in Machine Learning Software Systems
Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha +1
Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and depl…
Data-access performance anti-patterns in data-intensive systems
Biruk Asmare Muse, Kawser Wazed Nafi, Foutse Khomh +1
Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional software, data-intensive systems are prone to t…
A Probabilistic Framework for Mutation Testing in Deep Neural Networks
Florian Tambon, Foutse Khomh, Giuliano Antoniol
Context: Mutation Testing (MT) is an important tool in traditional Software Engineering (SE) white-box testing. It aims to artificially inject faults in a system to evaluate a test…
A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks
Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh
Software testing activities scrutinize the artifacts and the behavior of a software product to find possible defects and ensure that the product meets its expected requirements. Re…
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…
Dev2vec: Representing Domain Expertise of Developers in an Embedding Space
Arghavan Moradi Dakhel, Michel C. Desmarais, Foutse Khomh
Accurate assessment of the domain expertise of developers is important for assigning the proper candidate to contribute to a project or to attend a job role. Since the potential ca…
Threat Assessment in Machine Learning based Systems
Lionel Nganyewou Tidjon, Foutse Khomh
Machine learning is a field of artificial intelligence (AI) that is becoming essential for several critical systems, making it a good target for threat actors. Threat actors exploi…
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…
Never trust, always verify : a roadmap for Trustworthy AI?
Lionel Nganyewou Tidjon, Foutse Khomh
Artificial Intelligence (AI) is becoming the corner stone of many systems used in our daily lives such as autonomous vehicles, healthcare systems, and unmanned aircraft systems. Ma…
GitHub Copilot AI pair programmer: Asset or Liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam +4
Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI an…
Bugs in Machine Learning-based Systems: A Faultload Benchmark
Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh +2
The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and…
Studying the Practices of Deploying Machine Learning Projects on Docker
Moses Openja, Forough Majidi, Foutse Khomh +2
Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code. St…
Technical Debts and Faults in Open-source Quantum Software Systems: An Empirical Study
Moses Openja, Mohammad Mehdi Morovati, Le An +2
Quantum computing is a rapidly growing field attracting the interest of both researchers and software developers. Supported by its numerous open-source tools, developers can now bu…
The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis
Lionel Nganyewou Tidjon, Foutse Khomh
Artificial Intelligence (AI) is transforming our daily life with several applications in healthcare, space exploration, banking and finance. These rapid progresses in AI have broug…
Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues
Mohamed Raed El aoun, Heng Li, Foutse Khomh +1
With the advance in quantum computing, quantum software becomes critical for exploring the full potential of quantum computing systems. Recently, quantum software engineering (QSE)…
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…
Bug Characteristics in Quantum Software Ecosystem
Mohamed Raed El aoun, Heng Li, Foutse Khomh +1
With the advance in quantum computing in recent years, quantum software becomes vital for exploring the full potential of quantum computing systems. Quantum programming is differen…