Foutse Khomh

Biography

Foutse Khomh is a full professor of software engineering at Polytechnique Montréal, a Canada CIFAR AI Chair – Trustworthy Machine Learning Software Systems, and an FRQ-IVADO Research Chair in Software Quality Assurance for Machine Learning Applications. Khomh completed a PhD in software engineering at Université de Montréal in 2011, for which he received an Award of Excellence. He was also awarded a CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize in 2019.

His research interests include software maintenance and evolution, machine learning systems engineering, cloud engineering, and dependable and trustworthy ML/AI. His work has received four Ten-year Most Influential Paper (MIP) awards, and six Best/Distinguished Paper Awards. He has served on the steering committee of numerous organizations in software engineering, including SANER (chair), MSR, PROMISE, ICPC (chair), and ICSME (vice-chair). He initiated and co-organized Polytechnique Montréal‘s Software Engineering for Machine Learning Applications (SEMLA) symposium and the RELENG (release engineering) workshop series.

Khomh co-founded the NSERC CREATE SE4AI: A Training Program on the Development, Deployment and Servicing of Artificial Intelligence-based Software Systems, and is a principal investigator for the DEpendable Explainable Learning (DEEL) project.

He also co-founded Confiance IA, a Quebec consortium focused on building trustworthy AI, and is on the editorial board of multiple international software engineering journals, including IEEE Software, EMSE and JSEP. He is a senior member of IEEE.

Current Students

Nanda Assobjio Brice Yvan

Master's Research - Polytechnique Montréal

Gabriel Laberge

PhD - Polytechnique Montréal

Github

forough majidi

PhD - Polytechnique Montréal

Website

mohammadhossein.malekpour@gmail.com

Mohammadhossein Malekpour

Master's Research - Polytechnique Montréal

Github

Elnathan Tiokou Tiokou Fangang

Mohamed Amine Merzouk

Postdoctorate - Polytechnique Montréal

Co-supervisor :

Arghavan Moradi Dakhel

Postdoctorate - Polytechnique Montréal

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Github

Ben Braiek Yasmine

Master's Research - Polytechnique Montréal

Publications

Towards a Reliable French Speech Recognition Tool for an Automated Diagnosis of Learning Disabilities

Jihene Rezgui

Félix Jobin

Younes Kechout

Chritine Turgeon

Dyslexia, characterized by severe challenges in reading and spelling acquisition, presents a substantial barrier to proficient literacy, res… (see more)ulting in significantly reduced reading speed (2 to 3 times slower) and diminished text comprehension. With a prevalence ranging from 5G to 10% in the population, early intervention by speech and language pathologists (SLPs) can mitigate dyslexia's effects, but the diagnosis bottleneck impedes timely support. To address this, we propose leveraging machine learning tools to expedite the diagnosis process, focusing on automating phonetic transcription, a critical step in dyslexia assessment. We investigated the practicality of two model configurations utilizing Google's speech-to-text API with children speech in evaluation scenarios and compared their results against transcriptions crafted by experts. The first configuration focuses on Google API's speech-to-text while the second integrates Phonemizer, a text-to-phonemes tool based on a dictionary. Results analysis indicate that our Google-Phonemizer model yields reading accuracies comparable to those computed from human-made transcriptions, offering promise for clinical application. These findings underscore the potential of AI-driven solutions to enhance dyslexia diagnosis efficiency, paving the way for improved accessibility to vital SLP services.

2024-05-28

2024 International Conference on Smart Applications, Communications and Networking (SmartNets) (published)

Mining Action Rules for Defect Reduction Planning

Khouloud Oueslati

gabriel laberge

Maxime Lamothe

Defect reduction planning plays a vital role in enhancing software quality and minimizing software maintenance costs. By training a black bo… (see more)x machine learning model and"explaining"its predictions, explainable AI for software engineering aims to identify the code characteristics that impact maintenance risks. However, post-hoc explanations do not always faithfully reflect what the original model computes. In this paper, we introduce CounterACT, a Counterfactual ACTion rule mining approach that can generate defect reduction plans without black-box models. By leveraging action rules, CounterACT provides a course of action that can be considered as a counterfactual explanation for the class (e.g., buggy or not buggy) assigned to a piece of code. We compare the effectiveness of CounterACT with the original action rule mining algorithm and six established defect reduction approaches on 9 software projects. Our evaluation is based on (a) overlap scores between proposed code changes and actual developer modifications; (b) improvement scores in future releases; and (c) the precision, recall, and F1-score of the plans. Our results show that, compared to competing approaches, CounterACT's explainable plans achieve higher overlap scores at the release level (median 95%) and commit level (median 85.97%), and they offer better trade-off between precision and recall (median F1-score 88.12%). Finally, we venture beyond planning and explore leveraging Large Language models (LLM) for generating code edits from our generated plans. Our results show that suggested LLM code edits supported by our plans are actionable and are more likely to pass relevant test cases than vanilla LLM code recommendations.

2024-05-22

ArXiv (preprint)

Generative AI in Software Engineering Must Be Human-Centered: The Copenhagen Manifesto

Daniel Russo

Sebastian Baltes

Niels van Berkel

Paris Avgeriou

Fabio Calefato

Beatriz Cabrero-Daniel

Gemma Catolino

Jürgen Cito

Neil Ernst

Thomas Fritz

Hideaki Hata

Reid Holmes

Maliheh Izadi

Mikkel Baun Kjærgaard

Grischa Liebel

Alberto Lluch Lafuente

Stefano Lambiase

Walid Maalej

Gail Murphy … (see 15 more)

Nils Brede Moe

Gabrielle O'Brien

Elda Paja

Mauro Pezzè

John Stouby Persson

Rafael Prikladnicki

Paul Ralph

Martin P. Robillard

Thiago Rocha Silva

Klaas-Jan Stol

Margaret-Anne Storey

Viktoria Stray

Paolo Tell

Christoph Treude

Bogdan Vasilescu

2024-05-01

Journal of Systems and Software (published)

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Bertie Vidgen

Adarsh Agrawal

Ahmed M. Ahmed

Victor Akinwande

Namir Al-nuaimi

Najla Alfaraj

Elie Alhajjar

Lora Aroyo

Trupti Bavalatti

Borhane Blili-Hamelin

K. Bollacker

Rishi Bomassani

Marisa Ferrara Boston

Sim'eon Campos

Kal Chakra

Canyu Chen

Cody Coleman

Zacharie Delpierre Coudert

Leon Strømberg Derczynski

Debojyoti Dutta … (see 77 more)

Ian Eisenberg

James R. Ezick

Heather Frase

Brian Fuller

Ram Gandikota

Agasthya Gangavarapu

Ananya Gangavarapu

James Gealy

Rajat Ghosh

James Goel

Usman Gohar

Sujata Goswami

Scott A. Hale

Wiebke Hutiri

Joseph Marvin Imperial

Surgan Jandial

Nicholas C. Judd

Felix Juefei-Xu

Bhavya Kailkhura

Hannah Rose Kirk

Kevin Klyman

Chris Knotz

Michael Kuchnik

Shachi H. Kumar

Chris Lengerich

Bo Li

Zeyi Liao

Eileen Peters Long

Victor Lu

Yifan Mai

Priyanka Mary Mammen

Kelvin Manyeki

Sean McGregor

Virendra Mehta

Shafee Mohammed

Emanuel Moss

Lama Nachman

Dinesh Jinenhally Naganna

Amin Nikanjam

Besmira Nushi

Luis Oala

Iftach Orr

Alicia Parrish

Çigdem Patlak

William Pietri

Forough Poursabzi-Sangdeh

Eleonora Presani

Fabrizio Puletti

Paul Rottger

Saurav Sahay

Tim Santos

Nino Scherrer

Alice Schoenauer Sebag

Patrick Schramowski

Abolfazl Shahbazi

Vin Sharma

Xudong Shen

Vamsi Sistla

Leonard Tang

Davide Testuggine

Vithursan Thangarasa

Elizabeth A Watkins

Rebecca Weiss

Christoper A. Welty

Tyler Wilbers

Adina Williams

Carole-Jean Wu

Poonam Yadav

Xianjun Yang

Yi Zeng

Wenhui Zhang

Fedor Zhdanov

Jiacheng Zhu

Percy Liang

Peter Mattson

Joaquin Vanschoren

2024-04-18

ArXiv (preprint)

Tackling the XAI Disagreement Problem with Regional Explanations

gabriel laberge

Yann Batiste Pequignot

Mario Marchand

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Machine Learning Robustness: A Primer

Houssem Ben Braiek

This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness … (see more)in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.

2024-04-01

ArXiv (preprint)

Bugs in Large Language Models Generated Code: An Empirical Study

Florian Tambon

Arghavan Moradi Dakhel

Amin Nikanjam

Michel C. Desmarais

Giuliano Antoniol

2024-03-13

ArXiv (preprint)

Assessing the Security of GitHub Copilot Generated Code - A Targeted Replication Study

Vahid Majdinasab

Michael Joshua Bishop

Shawn Rasheed

Arghavan Moradi Dakhel

Amjed Tahir

2024-03-12

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

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… (see more)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.

2024-03-12

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

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

Xingfang Wu

Heng Li

Nobukazu Yoshioka

Hironori Washizaki

2024-03-12

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

ChatGPT vs LLaMA: Impact, Reliability, and Challenges in Stack Overflow Discussions

Léuson M. P. Da Silva

Jordan Samhi

Since its release in November 2022, ChatGPT has shaken up Stack Overflow, the premier platform for developers' queries on programming and so… (see more)ftware development. Demonstrating an ability to generate instant, human-like responses to technical questions, ChatGPT has ignited debates within the developer community about the evolving role of human-driven platforms in the age of generative AI. Two months after ChatGPT's release, Meta released its answer with its own Large Language Model (LLM) called LLaMA: the race was on. We conducted an empirical study analyzing questions from Stack Overflow and using these LLMs to address them. This way, we aim to (ii) measure user engagement evolution with Stack Overflow over time; (ii) quantify the reliability of LLMs' answers and their potential to replace Stack Overflow in the long term; (iii) identify and understand why LLMs fails; and (iv) compare LLMs together. Our empirical results are unequivocal: ChatGPT and LLaMA challenge human expertise, yet do not outperform it for some domains, while a significant decline in user posting activity has been observed. Furthermore, we also discuss the impact of our findings regarding the usage and development of new LLMs.

2024-02-13

ArXiv (preprint)

AITA: AI trustworthiness assessment

Bertrand Braunschweig

Stefan Buijsman

Faicel Chamroukhi

Fredrik Heintz

Juliette Mattioli

Maximilian Poretschkin

2024-01-03

AI and Ethics (published)