Foutse Khomh

houssem.ben-braiek@polymtl.ca

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

forough.majidi@mila.quebec

Site web

anthonia-oluchukwu.njoku@mila.quebec

Anthonia Oluchukwu Njoku

Doctorat - Polytechnique

khouloud.oueslati@mila.quebec

Khouloud Oueslati

Maîtrise recherche - Polytechnique

Arian Qazvini

Maîtrise recherche - Polytechnique

arian.qazvini@mila.quebec

Site web

Github

Elnathan Tiokou Tiokou Fangang

Maîtrise recherche - Polytechnique

elnathan.tiokou@mila.quebec

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

yasmine.ben-braiek@mila.quebec

Publications

Evaluating machine learning-driven intrusion detection systems in IoT: Performance and energy consumption

Saeid Jamshidi

Kawser Wazed Nafi

Amin Nikanjam

2025-05-31

Computers & Industrial Engineering (publié)

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs

Roozbeh Aghili

Xingfang Wu

Heng Li

2025-05-19

ArXiv (prépublication)

Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning

Ruchira Manke

Mohammad Wardat

Hridesh Rajan

While deep learning (DL) has permeated, and become an integral component of many critical software systems, today software engineering resea… (voir plus)rch hasn't explored how to separately test data and models that are integral for DL approaches to work effectively. The main challenge in independently testing these components arises from the tight dependency between data and models. This research explores this gap, introducing our methodology of mock deep testing for unit testing of DL applications. To enable unit testing, we introduce a design paradigm that decomposes the workflow into distinct, manageable components, minimizes sequential dependencies, and modularizes key stages of the DL. For unit testing these components, we propose modeling their dependencies using mocks. This modular approach facilitates independent development and testing of the components, ensuring comprehensive quality assurance throughout the development process. We have developed KUnit, a framework for enabling mock deep testing for the Keras library. We empirically evaluated KUnit to determine the effectiveness of mocks. Our assessment of 50 DL programs obtained from Stack Overflow and GitHub shows that mocks effectively identified 10 issues in the data preparation stage and 53 issues in the model design stage. We also conducted a user study with 36 participants using KUnit to perceive the effectiveness of our approach. Participants using KUnit successfully resolved 25 issues in the data preparation stage and 38 issues in the model design stage. Our findings highlight that mock objects provide a lightweight emulation of the dependencies for unit testing, facilitating early bug detection. Lastly, to evaluate the usability of KUnit, we conducted a post-study survey. The results reveal that KUnit is helpful to DL application developers, enabling them to independently test each component effectively in different stages.

2025-05-05

2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE) (publié)

Kernel-Level Event-Based Performance Anomaly Detection in Software Systems under Varying Load Conditions

Anthonia Njoku

Heng Li

2025-05-04

Companion of the 16th ACM/SPEC International Conference on Performance Engineering (publié)

JPerfEvo: A Tool for Tracking Method-Level Performance Changes in Java Projects

Kaveh Shahedi

Maxime Lamothe

Heng Li

Performance regressions and improvements are common phenomena in software development, occurring periodically as software evolves and mature… (voir plus)s. When developers introduce new changes to a program’s codebase, unforeseen performance variations may arise. Identifying these changes at the method level, however, can be challenging due to the complexity and scale of modern codebases. In this work, we present JPerfEvo, a tool designed to automate the evaluation of the method-level performance impact of each code commit (i.e., the performance variations between the two versions before and after a commit). Leveraging the Java Microbenchmark Harness (JMH) module for benchmarking the modified methods, JPerfEvo instruments their execution and applies robust statistical evaluations to detect performance changes. The tool can classify these changes as performance improvements, regressions, or neutral (i.e., no change), with the change magnitude. We evaluated JPerfEvo on three popular and mature open-source Java projects, demonstrating its effectiveness in identifying performance changes throughout their development histories.

2025-04-27

IEEE Working Conference on Mining Software Repositories (publié)

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

Franccois Belias

Leuson Da Silva

Cyrine Zid

2025-04-27

ArXiv (prépublication)

Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications

Patrick Loic Foalem

Leuson Da Silva

Heng Li

Ettore Merlo

Machine learning (ML) is increasingly applied across industries to automate decision-making, but concerns about ethical and legal compliance… (voir plus) remain due to limited transparency, fairness, and accountability. Monitoring through logging a long-standing practice in traditional software offers a potential means for auditing ML applications, as logs provide traceable records of system behavior useful for debugging, performance analysis, and continuous auditing. systematically auditing models for compliance or accountability. The findings underscore the need for enhanced logging practices and tooling that systematically integrate responsible AI metrics. Such practices would support the development of auditable, transparent, and ethically responsible ML systems, aligning with growing regulatory requirements and societal expectations. By highlighting specific deficiencies and opportunities, this work provides actionable guidance for both practitioners and tool developers seeking to strengthen the accountability and trustworthiness of ML applications.

2025-04-13

Empirical Software Engineering (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.

2025-04-08

ArXiv (prépublication)

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

Vahid Majdinasab

Amin Nikanjam

2025-04-06

ArXiv (prépublication)