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

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 (published)

Bug characterization in machine learning-based systems

Mohammad Mehdi Morovati

Amin Nikanjam

Florian Tambon

Z. Jiang

2023-12-05

Empirical Software Engineering (published)

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… (see more)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 (published)

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 (published)

Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow

Florian Tambon

Amin Nikanjam

Le An

Giuliano Antoniol

2023-11-29

Empirical Software Engineering (published)

An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software

Aaditya Bhatia

Bram Adams

Ahmed E. Hassan

The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algori… (see more)thms with minimal overhead. However, during this accelerated ML development process, said developers may often make sub-optimal design and implementation decisions, leading to the introduction of technical debt that, if not addressed promptly, can have a significant impact on the quality of the ML-based software. Developers frequently acknowledge these sub-optimal design and development choices through code comments during software development. These comments, which often highlight areas requiring additional work or refinement in the future, are known as self-admitted technical debt (SATD). This paper aims to investigate SATD in ML code by analyzing 318 open-source ML projects across five domains, along with 318 non-ML projects. We detected SATD in source code comments throughout the different project snapshots, conducted a manual analysis of the identified SATD sample to comprehend the nature of technical debt in the ML code, and performed a survival analysis of the SATD to understand the evolution of such debts. We observed: i) Machine learning projects have a median percentage of SATD that is twice the median percentage of SATD in non-machine learning projects. ii) ML pipeline components for data preprocessing and model generation logic are more susceptible to debt than model validation and deployment components. iii) SATDs appear in ML projects earlier in the development process compared to non-ML projects. iv) Long-lasting SATDs are typically introduced during extensive code changes that span multiple files exhibiting low complexity.

2023-11-20

ArXiv (preprint)

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

Vahid Majdinasab

Michael Joshua Bishop

Shawn Rasheed

Arghavan Moradi Dakhel

Amjed Tahir