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

Nanda Assobjio Brice Yvan

Maîtrise recherche - Polytechnique

Ahmed Haj Yahmed

Maîtrise recherche - Polytechnique

Gabriel Laberge

Doctorat - Polytechnique

forough majidi

Doctorat - Polytechnique

Site web

mohammadhossein.malekpour@gmail.com

Mohammadhossein Malekpour

Maîtrise recherche - Polytechnique

Elnathan Tiokou Tiokou Fangang

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Arghavan Moradi Dakhel

Postdoctorat - Polytechnique

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

Publications

Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study

Mohammad Mehdi Morovati

Florian Tambon

Mina Taraghi

Amin Nikanjam

2024-01-01

Empir. Softw. Eng. (publié)

Data-access performance anti-patterns in data-intensive systems

Biruk Asmare Muse

Kawser Wazed Nafi

Giuliano Antoniol

Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional softwar… (voir plus)e, data-intensive systems are prone to technical debts introduced to cope-up with the pressure of time and resource constraints on developers. Data-access is a critical component of data-intensive systems as it determines the overall performance and functionality of such systems. While data access technical debts are getting attention from the research community, technical debts affecting the performance, are not well investigated. Objective: Identify, categorize, and validate data access performance issues in the context of NoSQL-based and polyglot persistence data-intensive systems using qualitative study. Method: We collect issues from NoSQL-based and polyglot persistence open-source data-intensive systems and identify data access performance issues using inductive coding and build a taxonomy of the root causes. Then, we validate the perceived relevance of the newly identified performance issues using a developer survey.

2024-01-01

Empir. Softw. Eng. (publié)

Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning

Saeid Jamshidi

Ashkan Amirnia

Amin Nikanjam

2024-01-01

Procedia Computer Science (publié)

List Comprehension Versus for Loops Performance in Real Python Projects: Should we Care?

Cyrine Zid

François Belias

Massimiliano Di Penta

Giuliano Antoniol

List comprehensions are a Pythonic functional construct allowing developers to express in a concise way loops to build and manipulate lists.… (voir plus) Previous studies point to a gain in speed when list comprehensions are adopted. This paper reports the results of a study that compares the execution time performance of Python code written using list comprehensions as opposed to equivalent imperative programming. To this aim, we have developed a set of transformation rules to map Python for loops into list comprehensions. On the one hand, on artificial code snippets, we found list comprehensions to be faster than procedural code, with differences becoming evident if amplifying the tests, i.e., executing the code fragment thousands of times. On the other hand, this does not happen when executing real-world Python projects, where the performance may or may not improve, depending on the projects' features and the nature of the manipulated objects.

2024-01-01

SANER (publié)

Triage Software Update Impact via Release Notes Classification

Solomon Berhe

Vanessa Kan

Omhier Khan

Nathan Pader

Ali Zain Farooqui

Marc Maynard

2024-01-01

Procedia Computer Science (publié)

Validation of Vigilance Decline Capability in A Simulated Test Environment: A Preliminary Step Towards Neuroadaptive Control

Andra Mahu

Amandeep Singh

Florian Tambon

Benoit Ouellette

Jean-françois Delisle

Tanya Paul

Alexandre Marois

Philippe Doyon-poulin

Vigilance is the ability to sustain attention. It is crucial in tasks like piloting and driving that involve the ability to sustain attentio… (voir plus)n. However, cognitive performance often falters with prolonged tasks, leading to reduced efficiency, slower reactions, and increased error likelihood. Identifying and addressing diminished vigilance is essential for enhancing driving safety. Neuro-physiological indicators have shown promising results to monitor vigilance, paving the way for neuroadaptive control of vigilance. In fact, the collection of vigilance-related physiological markers could allow, using neuroadaptive intelligent systems, a real-time adaption of tasks or the presentation of countermeasures to prevent errors that would ensue from such hypovigilant situations. Before reaching this goal, one must however collect valid data truly representative of hypovigilance which, in turn, can be used to develop prediction models of the vigilant state. This study serves as a proof of concept to assess validity of a testbed to induce and measure vigilance decline through a simulated test environment, validating controlled induction, and evaluating its impact on participants’ performance and subjective experiences. In total, 28 participants (10 females, 18 males) aged 18 to 35 (M = 23.75 years), were recruited. All participants held valid driving licenses and had corrected-to-normal vision. Data collection involved Psychomotor Vigilance Task (PVT), Karolinska Sleepiness Scale (KSS) and the Stanford Sleepiness Scale (SSS) along with neuro-physiological specialized equipment: Enobio 8 EEG, Empatica E4, Polar H10 and Tobii Nano Pro eye tracker. Notably, this study is limited to demonstrating the results of PVT, KSS, and SSS, with the aim of assessing the effectiveness of the test setup. Participants self-reported their loss of vigilance by pressing a marker on the steering wheel. To induce hypovigilance, participants drove an automatic car in a low-traffic, monotonous environment for 60 minutes, featuring empty fields of grass and desert, employing specific in-game procedures. The driving task included instructions for lane-keeping, indicator usage, and maintaining speeds of up to 80 km/h, with no traffic lights or stop signs present. Experiments were conducted before lunch, between 9 am and 12 pm, ensuring maximum participant alertness, with instructions to abstain from caffeine, alcohol, nicotine, and cannabis on the experiment day. Results showed that the mean reaction time (RT) increased from 257.7 ms before driving to 276.8 ms after driving, t = 4.82, p .0001, d = -0.61 whereas the median RT changed from 246.07 ms to 260.89 ms, t = 3.58, p = 0.0013, d= -0.53 indicating a statistically significant alteration in participant's psychomotor performance. The mean number of minor lapses in attention (RT >500ms) to the PVT increased from 1.11 before driving to 1.67 after driving, but was not statistically significant t = 1.66, p = 0.11, d = -0.28. KSS showed a considerable rise of sleepiness, with a mean of 4.11 (rather alert) before driving increasing to 5.96 (some signs of sleepiness) after driving, t = 5.65, p .0001, d = -1.04. Similarly, the SSS demonstrated an increase in mean values from 2.57 (able to concentrate) before driving to 3.96 (somewhat foggy) after driving, t = 8.42, p .0001, d = -1.20, signifying an increased perception of sleepiness following the driving activity. Lastly, the mean time of the first marker press was 17:38 minutes (SD = 9:47 minutes) indicating that the self-reported loss of vigilance occurred during the first 30 minutes of the driving task. The observed increase in PVT reaction time aligns with the declined alertness reported on both the KSS and SSS responses, suggesting a consistent decline in vigilance and alertness post-driving. In conclusion, the study underscores the effectiveness and validity of the simulated test environment in inducing vigilance decline, providing valuable insights into the impact on both objective and subjective measures. At the same time, the research sets the stage for exploring neuroadaptive control strategies, aiming to enhance task performance and safety. Ultimately, this will contribute to the development of a non-invasive artificial intelligence system capable of detecting vigilance states in extreme/challenging environments, e.g. for pilots and drivers.

2024-01-01

Neuroergonomics and Cognitive Engineering (publié)

Harnessing Pre-trained Generalist Agents for Software Engineering Tasks

Paulina Stevia Nouwou Mindom

Amin Nikanjam

Nowadays, we are witnessing an increasing adoption of Artificial Intelligence (AI) to develop techniques aimed at improving the reliability,… (voir plus) effectiveness, and overall quality of software systems. Deep reinforcement learning (DRL) has recently been successfully used for automation in complex tasks such as game testing and solving the job-shop scheduling problem. However, these specialized DRL agents, trained from scratch on specific tasks, suffer from a lack of generalizability to other tasks and they need substantial time to be developed and re-trained effectively. Recently, DRL researchers have begun to develop generalist agents, able to learn a policy from various environments and capable of achieving performances similar to or better than specialist agents in new tasks. In the Natural Language Processing or Computer Vision domain, these generalist agents are showing promising adaptation capabilities to never-before-seen tasks after a light fine-tuning phase and achieving high performance. This paper investigates the potential of generalist agents for solving SE tasks. Specifically, we conduct an empirical study aimed at assessing the performance of two generalist agents on two important SE tasks: the detection of bugs in games (for two games) and the minimization of makespan in a scheduling task, to solve the job-shop scheduling problem (for two instances). Our results show that the generalist agents outperform the specialist agents with very little effort for fine-tuning, achieving a 20% reduction of the makespan over specialized agent performance on task-based scheduling. In the context of game testing, some generalist agent configurations detect 85% more bugs than the specialist agents. Building on our analysis, we provide recommendations for researchers and practitioners looking to select generalist agents for SE tasks, to ensure that they perform effectively.

2023-12-24

ArXiv (prépublication)

Studying the Practices of Testing Machine Learning Software in the Wild

Moses Openja

Armstrong Foundjem

Zhen Ming Jiang

Mouna Abidi

Ahmed E. Hassan

Background: We are witnessing an increasing adoption of machine learning (ML), especially deep learning (DL) algorithms in many software sys… (voir plus)tems, including safety-critical systems such as health care systems or autonomous driving vehicles. Ensuring the software quality of these systems is yet an open challenge for the research community, mainly due to the inductive nature of ML software systems. Traditionally, software systems were constructed deductively, by writing down the rules that govern the behavior of the system as program code. However, for ML software, these rules are inferred from training data. Few recent research advances in the quality assurance of ML systems have adapted different concepts from traditional software testing, such as mutation testing, to help improve the reliability of ML software systems. However, it is unclear if any of these proposed testing techniques from research are adopted in practice. There is little empirical evidence about the testing strategies of ML engineers. Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing practices in the wild, to identify the ML properties being tested, the followed testing strategies, and their implementation throughout the ML workflow. Method: First, we systematically summarized the different testing strategies (e.g., Oracle Approximation), the tested ML properties (e.g., Correctness, Bias, and Fairness), and the testing methods (e.g., Unit test) from the literature. Then, we conducted a study to understand the practices of testing ML software. Results: In our findings: 1) we identified four (4) major categories of testing strategy including Grey-box, White-box, Black-box, and Heuristic-based techniques that are used by the ML engineers to find software bugs. 2) We identified 16 ML properties that are tested in the ML workflow.

2023-12-19

ArXiv (prépublication)

Harnessing Predictive Modeling and Software Analytics in the Age of LLM-Powered Software Development (Invited Talk)

2023-12-08

International Conference on Predictive Models in Software Engineering (publié)

Bug characterization in machine learning-based systems

Mohammad Mehdi Morovati

Amin Nikanjam

Florian Tambon

Z. Jiang

2023-12-05

Empirical Software Engineering (publié)

A Machine Learning Based Approach to Detect Machine Learning Design Patterns

Weitao Pan

Hironori Washizaki

Nobukazu Yoshioka

Yoshiaki Fukazawa

Yann‐Gaël Guéhéneuc

As machine learning expands to various domains, the demand for reusable solutions to similar problems increases. Machine learning design pat… (voir plus)terns are reusable solutions to design problems of machine learning applications. They can significantly enhance programmers' productivity in programming that requires machine learning algorithms. Given the critical role of machine learning design patterns, the automated detection of them becomes equally vital. However, identifying design patterns can be time-consuming and error-prone. We propose an approach to detect their occurrences in Python files. Our approach uses an Abstract Syntax Tree (AST) of Python files to build a corpus of data and train a refined Text-CNN model to automatically identify machine learning design patterns. We empirically validate our approach by conducting an exploratory study to detect four common machine learning design patterns: Embedding, Multilabel, Feature Cross, and Hashed Feature. We manually label 450 Python code files containing these design patterns from repositories of projects in GitHub. Our approach achieves accuracy values ranging from 80 % to 92% for each of the four patterns.

2023-12-04

Asia-Pacific Software Engineering Conference (publié)

A large-scale exploratory study of android sports apps in the google play store

Bhagya Chembakottu

Heng Li

2023-12-01

Information and Software Technology (publié)