Foutse Khomh

Biographie

Foutse Khomh est professeur titulaire de génie logiciel à Polytechnique Montréal, titulaire d'une chaire en IA Canada-CIFAR dans le domaine des systèmes logiciels d'apprentissage automatique fiables, et titulaire d'une chaire de recherche FRQ-IVADO sur l'assurance qualité des logiciels pour les applications d'apprentissage automatique.

Il a obtenu un doctorat en génie logiciel de l'Université de Montréal en 2011, avec une bourse d'excellence. Il a également reçu le prix CS-Can/Info-Can du meilleur jeune chercheur en informatique en 2019. Ses recherches portent sur la maintenance et l'évolution des logiciels, l'ingénierie des systèmes d'apprentissage automatique, l'ingénierie en nuage et l’IA/apprentissage automatique fiable et digne de confiance.

Ses travaux ont été récompensés par quatre prix de l’article le plus important Most Influential Paper en dix ans et six prix du meilleur article ou de l’article exceptionnel (Best/Distinguished Paper). Il a également siégé au comité directeur de plusieurs conférences et rencontres : SANER (comme président), MSR, PROMISE, ICPC (comme président) et ICSME (en tant que vice-président). Il a initié et coorganisé le symposium Software Engineering for Machine Learning Applications (SEMLA) et la série d'ateliers Release Engineering (RELENG).

Il est cofondateur du projet CRSNG CREATE SE4AI : A Training Program on the Development, Deployment, and Servicing of Artificial Intelligence-based Software Systems et l'un des chercheurs principaux du projet Dependable Explainable Learning (DEEL). Il est également cofondateur de l'initiative québécoise sur l'IA digne de confiance (Confiance IA Québec). Il fait partie du comité de rédaction de plusieurs revues internationales de génie logiciel (dont IEEE Software, EMSE, JSEP) et est membre senior de l'Institute of Electrical and Electronics Engineers (IEEE).

Étudiants actuels

Houssem Ben Braiek

Postdoctorat - Polytechnique

Doctorat - Polytechnique

Github

forough majidi

Doctorat - Polytechnique

Site web

Khouloud Oueslati

Maîtrise recherche - Polytechnique

Arian Qazvini

Maîtrise recherche - Polytechnique

Site web

Github

Elnathan Tiokou Tiokou Fangang

Maîtrise recherche - Polytechnique

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

Publications

Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing

Seif Mzoughi

Mohamed Elshafeia

2025-03-31

arXiv (publié)

Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things

Saeid Jamshidi

Amin Nikanjam

Kawser Wazed Nafi

As the Internet of Things (IoT) continues to expand, ensuring the security of connected devices has become increasingly critical. Traditiona… (voir plus)l Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks. This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments. We provide a thorough overview of various IDS deployment strategies and categorize the types of intrusions common in IoT systems. A range of ML methods -- including Support Vector Machines, Naive Bayes, K-Nearest Neighbors, Decision Trees, and Random Forests -- are examined alongside advanced DL models such as LSTM, CNN, Autoencoders, RNNs, and Deep Belief Networks. Each technique is evaluated based on its accuracy, efficiency, and suitability for real-world IoT applications. We also address major challenges such as high false positive rates, data imbalance, encrypted traffic analysis, and the resource constraints of IoT devices. In addition, we highlight the emerging role of Generative AI and Large Language Models (LLMs) in improving threat detection, automating responses, and generating intelligent security policies. Finally, we discuss ethical and privacy concerns, underscoring the need for responsible and transparent implementation. This paper aims to provide a comprehensive framework for developing adaptive, intelligent, and secure IDS solutions tailored for the evolving landscape of IoT.

2025-03-31

arXiv (publié)

Performance Smells in ML and Non-ML Python Projects: A Comparative Study

Franccois Belias

Leuson Da Silva

Cyrine Zid

2025-03-31

arXiv (publié)

Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search

Vahid Majdinasab

Amin Nikanjam

2025-03-31

arXiv (publié)

Towards Assessing Deep Learning Test Input Generators

Seif Mzoughi

Ahmed Haj Yahmed

Mohamed Elshafei

Diego Elias Costa

2025-03-31

arXiv (publié)

Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems

Dmytro Humeniuk

2025-03-25

ArXiv (prépublication)

Understanding the impact of IoT security patterns on CPU usage and energy consumption: a dynamic approach for selecting patterns with deep reinforcement learning

Saeid Jamshidi

Amin Nikanjam

Kawser Wazed Nafi

2025-03-09

International Journal of Information Security (publié)

Self-adaptive cyber defense for sustainable IoT: A DRL-based IDS optimizing security and energy efficiency

Saeid Jamshidi

Ashkan Amirnia

Amin Nikanjam

Kawser Wazed Nafi

Samira Keivanpour

2025-02-28

Journal of Network and Computer Applications (publié)

A Taxonomy of Inefficiencies in LLM-Generated Python Code

Altaf Allah Abbassi

Leuson Da Silva

Amin Nikanjam

Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed L… (voir plus)LM-generated code and identified various quality issues- such as redundancy, poor maintainability, and sub-optimal performance- a systematic understanding and categorization of these inefficiencies remain unexplored. Therefore, we empirically investigate inefficiencies in LLM-generated Python code by state-of-the-art models, i.e., CodeLlama, DeepSeek-Coder, and CodeGemma. To do so, we manually analyze 492 generated Python code snippets in the HumanEval+ dataset. We then construct a taxonomy of inefficiencies in LLM-generated Python code that includes 5 categories (General Logic, Performance, Readability, Maintainability, and Errors) and 19 subcategories of inefficiencies. We validate the obtained taxonomy through an online survey with 58 LLM practitioners and researchers. The surveyed participants affirmed the completeness of the proposed taxonomy, and the relevance and the popularity of the identified code inefficiency patterns. Our qualitative findings indicate that inefficiencies are diverse and interconnected, affecting multiple aspects of code quality, with logic and performance-related inefficiencies being the most frequent and often co-occurring while impacting overall code quality. Our taxonomy provides a structured basis for evaluating the quality of LLM-generated code and guiding future research to improve code generation efficiency.

2025-02-28

arXiv (publié)

Assessing the adoption of security policies by developers in terraform across different cloud providers

Alexandre Verdet

Mohammad Hamdaqa

Leuson Da Silva

Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to manage and … (voir plus)configure cloud infrastructure using scripts. However, the scripting process does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks. As a result, ensuring security relies on practitioners’ understanding and the adoption of explicit policies. To understand how practitioners deal with this problem, we perform an empirical study analyzing the adoption of scripted security best practices present in Terraform files, applied on AWS, Azure, and Google Cloud. We assess the adoption of these practices by analyzing a sample of 812 open-source GitHub projects. We scan each project’s configuration files, looking for policy implementation through static analysis (Checkov and Tfsec). The category Access policy emerges as the most widely adopted in all providers, while Encryption at rest presents the most neglected policies. Regarding the cloud providers, we observe that AWS and Azure present similar behavior regarding attended and neglected policies. Finally, we provide guidelines for cloud practitioners to limit infrastructure vulnerability and discuss further aspects associated with policies that have yet to be extensively embraced within the industry.

2025-02-26

Empirical Software Engineering (publié)

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons

Shaona Ghosh

Heather Frase

Adina Williams

Sarah Luger

Paul Rottger

Fazl Barez

Sean McGregor

Kenneth Fricklas

Mala Kumar

Quentin Feuillade--Montixi

Kurt Bollacker

Felix Friedrich

Ryan Tsang

Bertie Vidgen

Alicia Parrish

Chris Knotz

Eleonora Presani

Jonathan Bennion

Marisa Ferrara Boston

Mike Kuniavsky … (voir 81 de plus)

Wiebke Hutiri

James Ezick

Malek Ben Salem

Rajat Sahay

Sujata Goswami

Usman Gohar

Ben Huang

Supheakmungkol Sarin

Elie Alhajjar

Canyu Chen

Roman Eng

K. Manjusha

Virendra Mehta

Eileen Peters Long

Murali Krishna Emani

Natan Vidra

Benjamin Rukundo

Abolfazl Shahbazi

Kongtao Chen

Rajat Ghosh

Vithursan Thangarasa

Pierre Peign'e

Abhinav Singh

Max Bartolo

Satyapriya Krishna

Mubashara Akhtar

Rafael Gold

Cody Coleman

Luis Oala

Vassil Tashev

Joseph Marvin Imperial

Amy Russ

Sasidhar Kunapuli

Nicolas Miailhe

Julien Delaunay

Bhaktipriya Radharapu

Rajat Shinde

Tuesday

Debojyoti Dutta

Declan Grabb

Ananya Gangavarapu

Saurav Sahay

Agasthya Gangavarapu

Patrick Schramowski

Stephen Singam

Tom David

Xudong Han

Priyanka Mary Mammen

Tarunima Prabhakar

Venelin Kovatchev

Ahmed M. Ahmed

Kelvin Manyeki

Sandeep Madireddy

Fedor Zhdanov

Joachim Baumann

N. Vasan

Xianjun Yang

Carlos Mougn

Jibin Rajan Varghese

Hussain Chinoy

Seshakrishna Jitendar

Manil Maskey

Claire V. Hardgrove

Tianhao Li

Aakash Gupta

Emil Joswin

Yifan Mai

Shachi H. Kumar

Çigdem Patlak

Kevin Lu

Vincent Alessi

Sree Bhargavi Balija

Chenhe Gu

Robert Sullivan

James Gealy

Matt Lavrisa

James Goel

Peter Mattson

Percy Liang

Joaquin Vanschoren

2025-02-18

ArXiv (prépublication)

Bugs in Large Language Models Generated Code: An Empirical Study

Florian Tambon

Arghavan Moradi Dakhel

Amin Nikanjam

Michel C. Desmarais

Giuliano Antoniol

Large Language Models (LLMs) for code have gained significant attention recently. They can generate code in different programming languages … (voir plus)based on provided prompts, fulfilling a long-lasting dream in Software Engineering (SE), i.e., automatic code generation. Similar to human-written code, LLM-generated code is prone to bugs, and these bugs have not yet been thoroughly examined by the community. Given the increasing adoption of LLM-based code generation tools (e.g., GitHub Copilot) in SE activities, it is critical to understand the characteristics of bugs contained in code generated by LLMs. This paper examines a sample of 333 bugs collected from code generated using three leading LLMs (i.e., CodeGen, PanGu-Coder, and Codex) and identifies the following 10 distinctive bug patterns: Misinterpretations, Syntax Error, Silly Mistake, Prompt-biased code, Missing Corner Case, Wrong Input Type, Hallucinated Object, Wrong Attribute, Incomplete Generation, and Non-Prompted Consideration. The bug patterns are presented in the form of a taxonomy. The identified bug patterns are validated using an online survey with 34 LLM practitioners and researchers. The surveyed participants generally asserted the significance and prevalence of the bug patterns. Researchers and practitioners can leverage these findings to develop effective quality assurance techniques for LLM-generated code. This study sheds light on the distinctive characteristics of LLM-generated code.

2025-02-12

Empirical Software Engineering (publié)