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

Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends

Mina Taraghi

Gianolli Dorcelus

Armstrong Foundjem

Florian Tambon

The ubiquity of large-scale Pre-Trained Models (PTMs) is on the rise, sparking interest in model hubs, and dedicated platforms for hosting P… (voir plus)TMs. Despite this trend, a comprehensive exploration of the challenges that users encounter and how the community leverages PTMs remains lacking. To address this gap, we conducted an extensive mixed-methods empirical study by focusing on discussion forums and the model hub of HuggingFace, the largest public model hub. Based on our qualitative analysis, we present a taxonomy of the challenges and benefits associated with PTM reuse within this community. We then conduct a quantitative study to track model-type trends and model documentation evolution over time. Our findings highlight prevalent challenges such as limited guidance for beginner users, struggles with model output comprehensibility in training or inference, and a lack of model understanding. We also identified interesting trends among models where some models maintain high upload rates despite a decline in topics related to them. Additionally, we found that despite the introduction of model documentation tools, its quantity has not increased over time, leading to difficulties in model comprehension and selection among users. Our study sheds light on new challenges in reusing PTMs that were not reported before and we provide recommendations for various stakeholders involved in PTM reuse.

2024-03-11

2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (publié)

Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection

Xingfang Wu

Heng Li

Nobukazu Yoshioka

Hironori Washizaki

2024-03-11

2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (publié)

LLMs and Stack Overflow discussions: Reliability, impact, and challenges

Leuson Da Silva

Jordan Samhi

2024-02-12

ArXiv (prépublication)

AITA: AI trustworthiness assessment

Bertrand Braunschweig

Stefan Buijsman

Faicel Chamroukhi

Fredrik Heintz

Juliette Mattioli

Maximilian Poretschkin

2024-01-02

AI and Ethics (publié)

AmbieGen at the SBFT 2024 Tool Competition - CPS-UAV Track

Dmytro Humeniuk

2023-12-31

SBFT@ICSE (publié)

AmbieGenVAE at the SBFT 2024 Tool Competition - Cyber-Physical Systems Track

Dmytro Humeniuk

2023-12-31

SBFT@ICSE (publié)

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

Mohammad Mehdi Morovati

Florian Tambon

Mina Taraghi

Amin Nikanjam

Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to … (voir plus)produce intelligent agents. Despite recent developments in DRL technology, the main challenges that developers face in the development of DRL applications are still unknown. To fill this gap, in this paper, we conduct a large-scale empirical study of 927 DRL-related posts extracted from Stack Overflow, the most popular Q&A platform in the software community. Through the process of labeling and categorizing extracted posts, we created a taxonomy of common challenges encountered in the development of DRL applications, along with their corresponding popularity levels. This taxonomy has been validated through a survey involving 65 DRL developers. Results show that at least 45% of developers experienced 18 of the 21 challenges identified in the taxonomy. The most frequent source of difficulty during the development of DRL applications are Comprehension, API usage, and Design problems, while Parallel processing, and DRL libraries/frameworks are classified as the most difficult challenges to address, with respect to the time required to receive an accepted answer. We hope that the research community will leverage this taxonomy to develop efficient strategies to address the identified challenges and improve the quality of DRL applications.

2023-12-31

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.

2023-12-31

Empir. Softw. Eng. (publié)

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

Saeid Jamshidi

Ashkan Amirnia

Amin Nikanjam

2023-12-31

Procedia Computer Science (publié)

GIST: Generated Inputs Sets Transferability in Deep Learning

Florian Tambon

Giuliano Antoniol

2023-12-31

ACM Trans. Softw. Eng. Methodol. (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.

2023-12-31

SANER (publié)

Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing

Dmytro Humeniuk

Giuliano Antoniol

Evolutionary search-based techniques are commonly used for testing autonomous robotic systems. However, these approaches often rely on compu… (voir plus)tationally expensive simulator-based models for test scenario evaluation. To improve the computational efficiency of the search-based testing, we propose augmenting the evolutionary search (ES) with a reinforcement learning (RL) agent trained using surrogate rewards derived from domain knowledge. In our approach, known as RIGAA (Reinforcement learning Informed Genetic Algorithm for Autonomous systems testing), we first train an RL agent to learn useful constraints of the problem and then use it to produce a certain part of the initial population of the search algorithm. By incorporating an RL agent into the search process, we aim to guide the algorithm towards promising regions of the search space from the start, enabling more efficient exploration of the solution space. We evaluate RIGAA on two case studies: maze generation for an autonomous ant robot and road topology generation for an autonomous vehicle lane keeping assist system. In both case studies, RIGAA converges faster to fitter solutions and produces a better test suite (in terms of average test scenario fitness and diversity). RIGAA also outperforms the state-of-the-art tools for vehicle lane keeping assist system testing, such as AmbieGen and Frenetic.

2023-12-31

ACM Trans. Softw. Eng. Methodol. (publié)