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

Ahmed Haj Yahmed

Master's Research - Polytechnique Montréal

Gabriel Laberge

PhD - Polytechnique Montréal

forough majidi

PhD - Polytechnique Montréal

Website

mohammadhossein.malekpour@gmail.com

Mohammadhossein Malekpour

Master's Research - Polytechnique Montréal

Elnathan Tiokou Tiokou Fangang

Arghavan Moradi Dakhel

Postdoctorate - Polytechnique Montréal

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Publications

Ranking code clones to support maintenance activities

Osama Ehsan

Ying Zou

Dong Qiu

2023-04-25

Empirical Software Engineering (published)

Bugs in machine learning-based systems: a faultload benchmark

Mohammad Mehdi Morovati

Amin Nikanjam

Z. Jiang

2023-04-05

Empirical Software Engineering (published)

Can Ensembling Preprocessing Algorithms Lead to Better Machine Learning Fairness?

Khaled Badran

Pierre-Olivier Côté

Amanda Kolopanis

Rached Bouchoucha

Antonio Collante

Diego Elias Costa

Emad Shihab

In this work, we evaluate three popular fairness preprocessing algorithms and investigate the potential for combining all algorithms into a … (see more)more robust preprocessing ensemble. We report on lessons learned that can help practitioners better select fairness algorithms for their models.

2023-04-01

Computer (published)

Machine learning application development: practitioners’ insights

Md Saidur Rahman

Alaleh Hamidi

Jinghui Cheng

Giuliano Antoniol

Hironori Washizaki

2023-03-30

Software Quality Journal (published)

A Probabilistic Framework for Mutation Testing in Deep Neural Networks

Florian Tambon

Giuliano Antoniol

2023-03-01

Information and Software Technology (published)

Refactoring practices in the context of data-intensive systems

Biruk Asmare Muse

Giuliano Antoniol

2023-02-16

Empirical Software Engineering (published)

AmbieGen tool at the SBST 2022 Tool Competition

Dmytro Humeniuk

Giuliano Antoniol

AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been… (see more) adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.

2023-02-03

Proceedings of the 15th Workshop on Search-Based Software Testing (published)

Fooling SHAP with Stealthily Biased Sampling

gabriel laberge

Ulrich Aivodji

Satoshi Hara

Mario Marchand

SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a speciﬁc input versus a … (see more)background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations. However, existing attacks focus solely on altering the black-box model itself. In this paper, we propose a complementary family of attacks that leave the model intact and manipulate SHAP explanations using stealthily biased sampling of the data points used to approximate expectations w.r.t the background distribution. In the context of fairness audit, we show that our attack can reduce the importance of a sensitive feature when explaining the difference in outcomes between groups, while remaining undetected. These results highlight the manipulability of SHAP explanations and encourage auditors to treat post-hoc explanations with skepticism.

2023-02-01

ICLR.cc/2023/Conference (poster)

openreview.net

Studying Logging Practice in Machine Learning-based Applications

Patrick Loic Foalem

Heng Li

Logging is a common practice in traditional software development. Several research works have been done to investigate the different charact… (see more)eristics of logging practices in traditional software systems (e.g., Android applications, JAVA applications, C/C++ applications). Nowadays, we are witnessing more and more development of Machine Learning-based applications (ML-based applications). Today, there are many popular libraries that facilitate and contribute to the development of such applications, among which we can mention: Pytorch, Tensorflow, Theano, MXNet, Scikit-Learn, Caffe, and Keras. Despite the popularity of ML, we don't have a clear understanding of logging practices in ML applications. In this paper, we aim to fill this knowledge gap and help ML practitioners understand the characteristics of logging in ML-based applications. In particular, we conduct an empirical study on 110 open-source ML-based applications. Through a quantitative analysis, we find that logging practice in ML-based applications is less pervasive than in traditional applications including Android, JAVA, and C/C++ applications. Furthermore, the majority of logging statements in ML-based applications are in info and warn levels, compared to traditional applications where info is the majority of logging statement in C/C++ application and debug, error levels constitute the majority of logging statement in Android application. We also perform a quantitative and qualitative analysis of a random sample of logging statements to understand where ML developers put most of logging statements and examine why and how they are using logging. These analyses led to the following observations: (i) ML developers put most of the logging statements in model training, and in non-ML components. (ii) Data and model management appear to be the main reason behind the introduction of logging statements in ML-based applications.

2023-01-10

ArXiv (preprint)

SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design

Houssem Ben Braiek

Ali Tfaily

Thomas Reid

Ciro Guida

2023-01-05

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (published)

AmbieGen: A Search-based Framework for Autonomous Systems Testing

Dmytro Humeniuk

Giuliano Antoniol

2023-01-01

Sci. Comput. Program. (published)

GitHub Copilot AI pair programmer: Asset or Liability?

Arghavan Moradi Dakhel

Vahid Majdinasab

Amin Nikanjam

Michel C. Desmarais

Z. Jiang

Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called… (see more) Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.

2023-01-01

J. Syst. Softw. (published)