Foutse Khomh

houssem.ben-braiek@polymtl.ca

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

Houssem Ben Braiek

Postdoctorate - Polytechnique Montréal

PhD - Polytechnique Montréal

forough.majidi@mila.quebec

Website

anthonia-oluchukwu.njoku@mila.quebec

Anthonia Oluchukwu Njoku

PhD - Polytechnique Montréal

khouloud.oueslati@mila.quebec

Khouloud Oueslati

Master's Research - Polytechnique Montréal

Arian Qazvini

Master's Research - Polytechnique Montréal

arian.qazvini@mila.quebec

Website

Github

Elnathan Tiokou Tiokou Fangang

Master's Research - Polytechnique Montréal

elnathan.tiokou@mila.quebec

Ben Braiek Yasmine

Master's Research - Polytechnique Montréal

yasmine.ben-braiek@mila.quebec

Publications

AITA: AI trustworthiness assessment

Bertrand Braunschweig

Stefan Buijsman

Faicel Chamroukhi

Fredrik Heintz

Juliette Mattioli

Maximilian Poretschkin

2024-01-02

AI and Ethics (published)

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

Dmytro Humeniuk

2023-12-31

SBFT@ICSE (published)

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

Dmytro Humeniuk

2023-12-31

SBFT@ICSE (published)

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 … (see more)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. (published)

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 (published)

Triage Software Update Impact via Release Notes Classification

Solomon Berhe

Vanessa Kan

Omhier Khan

Nathan Pader

Ali Zain Farooqui

Marc Maynard

2023-12-31

Procedia Computer Science (published)

Validation of Vigilance Decline Capability in A Simulated Test Environment: A Preliminary Step Towards Neuroadaptive Control

Andra Mahu

Amandeep Singh

Florian Tambon

Benoit Ouellette

Jean-françois Delisle

Tanya Paul

Alexandre Marois

Philippe Doyon-poulin

Vigilance is the ability to sustain attention. It is crucial in tasks like piloting and driving that involve the ability to sustain attentio… (see more)n. However, cognitive performance often falters with prolonged tasks, leading to reduced efficiency, slower reactions, and increased error likelihood. Identifying and addressing diminished vigilance is essential for enhancing driving safety. Neuro-physiological indicators have shown promising results to monitor vigilance, paving the way for neuroadaptive control of vigilance. In fact, the collection of vigilance-related physiological markers could allow, using neuroadaptive intelligent systems, a real-time adaption of tasks or the presentation of countermeasures to prevent errors that would ensue from such hypovigilant situations. Before reaching this goal, one must however collect valid data truly representative of hypovigilance which, in turn, can be used to develop prediction models of the vigilant state. This study serves as a proof of concept to assess validity of a testbed to induce and measure vigilance decline through a simulated test environment, validating controlled induction, and evaluating its impact on participants’ performance and subjective experiences. In total, 28 participants (10 females, 18 males) aged 18 to 35 (M = 23.75 years), were recruited. All participants held valid driving licenses and had corrected-to-normal vision. Data collection involved Psychomotor Vigilance Task (PVT), Karolinska Sleepiness Scale (KSS) and the Stanford Sleepiness Scale (SSS) along with neuro-physiological specialized equipment: Enobio 8 EEG, Empatica E4, Polar H10 and Tobii Nano Pro eye tracker. Notably, this study is limited to demonstrating the results of PVT, KSS, and SSS, with the aim of assessing the effectiveness of the test setup. Participants self-reported their loss of vigilance by pressing a marker on the steering wheel. To induce hypovigilance, participants drove an automatic car in a low-traffic, monotonous environment for 60 minutes, featuring empty fields of grass and desert, employing specific in-game procedures. The driving task included instructions for lane-keeping, indicator usage, and maintaining speeds of up to 80 km/h, with no traffic lights or stop signs present. Experiments were conducted before lunch, between 9 am and 12 pm, ensuring maximum participant alertness, with instructions to abstain from caffeine, alcohol, nicotine, and cannabis on the experiment day. Results showed that the mean reaction time (RT) increased from 257.7 ms before driving to 276.8 ms after driving, t = 4.82, p < .0001, d = -0.61 whereas the median RT changed from 246.07 ms to 260.89 ms, t = 3.58, p = 0.0013, d= -0.53 indicating a statistically significant alteration in participant's psychomotor performance. The mean number of minor lapses in attention (RT >500ms) to the PVT increased from 1.11 before driving to 1.67 after driving, but was not statistically significant t = 1.66, p = 0.11, d = -0.28. KSS showed a considerable rise of sleepiness, with a mean of 4.11 (rather alert) before driving increasing to 5.96 (some signs of sleepiness) after driving, t = 5.65, p < .0001, d = -1.04. Similarly, the SSS demonstrated an increase in mean values from 2.57 (able to concentrate) before driving to 3.96 (somewhat foggy) after driving, t = 8.42, p < .0001, d = -1.20, signifying an increased perception of sleepiness following the driving activity. Lastly, the mean time of the first marker press was 17:38 minutes (SD = 9:47 minutes) indicating that the self-reported loss of vigilance occurred during the first 30 minutes of the driving task. The observed increase in PVT reaction time aligns with the declined alertness reported on both the KSS and SSS responses, suggesting a consistent decline in vigilance and alertness post-driving. In conclusion, the study underscores the effectiveness and validity of the simulated test environment in inducing vigilance decline, providing valuable insights into the impact on both objective and subjective measures. At the same time, the research sets the stage for exploring neuroadaptive control strategies, aiming to enhance task performance and safety. Ultimately, this will contribute to the development of a non-invasive artificial intelligence system capable of detecting vigilance states in extreme/challenging environments, e.g. for pilots and drivers.

2023-12-31

Neuroergonomics and Cognitive Engineering (published)

Detection and evaluation of bias-inducing features in machine learning

Moses Openja

Gabriel Laberge

2023-12-12

Empirical Software Engineering (published)

Harnessing Predictive Modeling and Software Analytics in the Age of LLM-Powered Software Development (Invited Talk)

2023-12-07

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

Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML… (see more) components, i.e., a software component operating based on ML. Understanding the bugs characteristics and maintenance challenges in ML-based systems can help developers of these systems to identify where to focus maintenance and testing efforts, by giving insights into the most error-prone components, most common bugs, etc. In this paper, we investigate the characteristics of bugs in ML-based software systems and the difference between ML and non-ML bugs from the maintenance viewpoint. We extracted 447,948 GitHub repositories that used one of the three most popular ML frameworks, i.e., TensorFlow, Keras, and PyTorch. After multiple filtering steps, we select the top 300 repositories with the highest number of closed issues. We manually investigate the extracted repositories to exclude non-ML-based systems. Our investigation involved a manual inspection of 386 sampled reported issues in the identified ML-based systems to indicate whether they affect ML components or not. Our analysis shows that nearly half of the real issues reported in ML-based systems are ML bugs, indicating that ML components are more error-prone than non-ML components. Next, we thoroughly examined 109 identified ML bugs to identify their root causes, symptoms, and calculate their required fixing time. The results also revealed that ML bugs have significantly different characteristics compared to non-ML bugs, in terms of the complexity of bug-fixing (number of commits, changed files, and changed lines of code). Based on our results, fixing ML bugs are more costly and ML components are more error-prone, compared to non-ML bugs and non-ML components respectively. Hence, paying a significant attention to the reliability of the ML components is crucial in ML-based systems.

2023-12-04

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.

2023-12-03

Asia-Pacific Software Engineering Conference (published)

A large-scale exploratory study of android sports apps in the google play store

Bhagya Chembakottu

Heng Li

2023-11-30

Information and Software Technology (published)