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

Collaborating Alumni - Polytechnique Montréal

Gabriel Laberge

PhD - Polytechnique Montréal

Github

forough majidi

PhD - Polytechnique Montréal

Website

mohammadhossein.malekpour@gmail.com

Mo Malekpour

Master's Research - Polytechnique Montréal

Website

Github

Elnathan Tiokou Tiokou Fangang

Mohamed Amine Merzouk

Postdoctorate - Polytechnique Montréal

Co-supervisor :

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Github

Ben Braiek Yasmine

Master's Research - Polytechnique Montréal

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… (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.

2024-01-24

ArXiv (preprint)

Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study

Mehil B. Shah

Mohammad Masudur Rahman

Context: Deep learning has achieved remarkable progress in various domains. However, like any software system, deep learning systems contain… (see more) bugs, some of which can have severe impacts, as evidenced by crashes involving autonomous vehicles. Despite substantial advancements in deep learning techniques, little research has focused on reproducing deep learning bugs, which is an essential step for their resolution. Existing literature suggests that only 3% of deep learning bugs are reproducible, underscoring the need for further research. Objective: This paper examines the reproducibility of deep learning bugs. We identify edit actions and useful information that could improve the reproducibility of deep learning bugs. Method: First, we construct a dataset of 668 deep-learning bugs from Stack Overflow and GitHub across three frameworks and 22 architectures. Second, out of the 668 bugs, we select 165 bugs using stratified sampling and attempt to determine their reproducibility. While reproducing these bugs, we identify edit actions and useful information for their reproduction. Third, we used the Apriori algorithm to identify useful information and edit actions required to reproduce specific types of bugs. Finally, we conducted a user study involving 22 developers to assess the effectiveness of our findings in real-life settings. Results: We successfully reproduced 148 out of 165 bugs attempted. We identified ten edit actions and five useful types of component information that can help us reproduce the deep learning bugs. With the help of our findings, the developers were able to reproduce 22.92% more bugs and reduce their reproduction time by 24.35%. Conclusions: Our research addresses the critical issue of deep learning bug reproducibility. Practitioners and researchers can leverage our findings to improve deep learning bug reproducibility.

2024-01-05

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)

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

Dmytro Humeniuk

2024-01-01

SBFT@ICSE (published)

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

Dmytro Humeniuk

2024-01-01

SBFT@ICSE (published)

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. (published)

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… (see more)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. (published)

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 (published)

GIST: Generated Inputs Sets Transferability in Deep Learning

Florian Tambon

Giuliano Antoniol

2024-01-01

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

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.… (see more) 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 (published)

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… (see more)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.

2024-01-01

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

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 (published)