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

Mo Malekpour

Master's Research - Polytechnique Montréal

Website

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

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)

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.

2024-01-01

Neuroergonomics and Cognitive Engineering (published)

Studying the Practices of Testing Machine Learning Software in the Wild

Moses Openja

Armstrong Foundjem

Zhen Ming (Jack) Jiang

Mouna Abidi

Ahmed E. Hassan

Background: We are witnessing an increasing adoption of machine learning (ML), especially deep learning (DL) algorithms in many software sys… (see more)tems, including safety-critical systems such as health care systems or autonomous driving vehicles. Ensuring the software quality of these systems is yet an open challenge for the research community, mainly due to the inductive nature of ML software systems. Traditionally, software systems were constructed deductively, by writing down the rules that govern the behavior of the system as program code. However, for ML software, these rules are inferred from training data. Few recent research advances in the quality assurance of ML systems have adapted different concepts from traditional software testing, such as mutation testing, to help improve the reliability of ML software systems. However, it is unclear if any of these proposed testing techniques from research are adopted in practice. There is little empirical evidence about the testing strategies of ML engineers. Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing practices in the wild, to identify the ML properties being tested, the followed testing strategies, and their implementation throughout the ML workflow. Method: First, we systematically summarized the different testing strategies (e.g., Oracle Approximation), the tested ML properties (e.g., Correctness, Bias, and Fairness), and the testing methods (e.g., Unit test) from the literature. Then, we conducted a study to understand the practices of testing ML software. Results: In our findings: 1) we identified four (4) major categories of testing strategy including Grey-box, White-box, Black-box, and Heuristic-based techniques that are used by the ML engineers to find software bugs. 2) We identified 16 ML properties that are tested in the ML workflow.

2023-12-19

ArXiv (preprint)

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)

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

In the rapidly evolving landscape of software development, Large Language Models (LLM) have emerged as powerful tools that can significantly… (see more) impact the way software code is written, reviewed, and optimized, making them invaluable resources for programmers. They offer developers the ability to leverage pre-trained knowledge and tap into vast code repositories, enabling faster development cycles and reducing the time spent on repetitive or mundane coding tasks. However, while these models offer substantial benefits, their adoption also presents multiple challenges. For example, they might generate code snippets that are syntactically correct but functionally flawed, requiring human review and validation. Moreover, the ethical considerations surrounding these models, such as biases in the training data, should be carefully addressed to ensure fair and inclusive software development practices. This talk will provide an overview and reflection on some of these challenges, present some preliminary solutions, and discuss opportunities for predictive models and data analytics.

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)

Detection and evaluation of bias-inducing features in machine learning

Moses Openja

gabriel laberge

2023-10-19

ArXiv (preprint)