Foutse Khomh

Biographie

Foutse Khomh est professeur titulaire de génie logiciel à Polytechnique Montréal, titulaire d'une chaire en IA Canada-CIFAR dans le domaine des systèmes logiciels d'apprentissage automatique fiables, et titulaire d'une chaire de recherche FRQ-IVADO sur l'assurance qualité des logiciels pour les applications d'apprentissage automatique.

Il a obtenu un doctorat en génie logiciel de l'Université de Montréal en 2011, avec une bourse d'excellence. Il a également reçu le prix CS-Can/Info-Can du meilleur jeune chercheur en informatique en 2019. Ses recherches portent sur la maintenance et l'évolution des logiciels, l'ingénierie des systèmes d'apprentissage automatique, l'ingénierie en nuage et l’IA/apprentissage automatique fiable et digne de confiance.

Ses travaux ont été récompensés par quatre prix de l’article le plus important Most Influential Paper en dix ans et six prix du meilleur article ou de l’article exceptionnel (Best/Distinguished Paper). Il a également siégé au comité directeur de plusieurs conférences et rencontres : SANER (comme président), MSR, PROMISE, ICPC (comme président) et ICSME (en tant que vice-président). Il a initié et coorganisé le symposium Software Engineering for Machine Learning Applications (SEMLA) et la série d'ateliers Release Engineering (RELENG).

Il est cofondateur du projet CRSNG CREATE SE4AI : A Training Program on the Development, Deployment, and Servicing of Artificial Intelligence-based Software Systems et l'un des chercheurs principaux du projet Dependable Explainable Learning (DEEL). Il est également cofondateur de l'initiative québécoise sur l'IA digne de confiance (Confiance IA Québec). Il fait partie du comité de rédaction de plusieurs revues internationales de génie logiciel (dont IEEE Software, EMSE, JSEP) et est membre senior de l'Institute of Electrical and Electronics Engineers (IEEE).

Étudiants actuels

Houssem Ben Braiek

Postdoctorat - Polytechnique

Doctorat - Polytechnique

Github

forough majidi

Doctorat - Polytechnique

Site web

Khouloud Oueslati

Maîtrise recherche - Polytechnique

Arian Qazvini

Maîtrise recherche - Polytechnique

Site web

Github

Elnathan Tiokou Tiokou Fangang

Maîtrise recherche - Polytechnique

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

Publications

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

Mehil B. Shah

Mohammad Masudur Rahman

2024-11-08

Empirical Software Engineering (publié)

Towards Optimizing SQL Generation via LLM Routing

Mohammadhossein Malekpour

Nour Shaheen

Amine Mhedhbi

Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capabl… (voir plus)e large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset.

2024-11-05

ArXiv (prépublication)

Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study

Doriane Olewicki

Leuson Da Silva

Suhaib Mujahid

Arezou Amini

Benjamin Mah

Marco Castelluccio

Sarra Habchi

Bram Adams

2024-10-31

arXiv (publié)

Doctoral Symposium Committee

Anthony Cleve

Christian Lange

Silvia Breu

Manar H. Alalfi

Mario Luca Bernardi

Cornelia Boldyreff

Marco D'Ambros

Simon Denier

Natalia Dragan

Ekwa Duala-Ekoko

Fausto Fasano

Adnane Ghannem

Carmine Gravino

Maen Hammad

Imed Hammouda

Salima Hassaine

Yue Jia

Zhen Ming (Jack) Jiang

Adam Kiezun … (voir 11 de plus)

Jay Kothari

Jonathan Memaitre

Naouel Moha

Rocco Oliveto

Denys Poshyvanyk

Michele Risi

Giuseppe Scanniello

Bonita Sharif

Andrew Sutton

Anis Yousefi

Eugenio Zimeo

Manar H. Alalfi Mario Luca Bernardi Cornelia Boldyreff Anthony Cleve Marco D'Ambros Simon Denier Natalia Dragan Ekwa Duala-Ekoko Fausto Fasa… (voir plus)no Adnane Ghannem Carmine Gravino Maen Hammad Imed Hammouda Salima Hassaine Yue Jia Zhen Ming Jiang Foutse Khomh Adam Kiezun Jay Kothari Jonathan Memaitre Naouel Moha Rocco Oliveto Denys Poshyvanyk Michele Risi Giuseppe Scanniello Bonita Sharif Andrew Sutton Anis Yousefi Eugenio Zimeo

2024-10-27

2024 IEEE 35th International Symposium on Software Reliability Engineering Workshops (ISSREW) (publié)

In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators

Dmytro Humeniuk

Houssem Ben Braiek

Thomas Reid

2024-10-26

Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (publié)

LIBS-Raman Multimodal Architecture for Automated Lunar Prospecting

Jérôme Pigeon

Richard Boudreault

Ahmed Ashraf

Pooneh Maghoul

2024-10-09

Earth and Space 2024 (publié)

Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization

Roozbeh Aghili

Qiaolin Qin

Heng Li

Web applications, accessible via web browsers over the Internet, facilitate complex functionalities without local software installation. In … (voir plus)the context of web applications, a workload refers to the number of user requests sent by users or applications to the underlying system. Existing studies have leveraged web application workloads to achieve various objectives, such as workload prediction and auto-scaling. However, these studies are conducted in an ad hoc manner, lacking a systematic understanding of the characteristics of web application workloads. In this study, we first conduct a systematic literature review to identify and analyze existing studies leveraging web application workloads. Our analysis sheds light on their workload utilization, analysis techniques, and high-level objectives. We further systematically analyze the characteristics of the web application workloads identified in the literature review. Our analysis centers on characterizing these workloads at two distinct temporal granularities: daily and weekly. We successfully identify and categorize three daily and three weekly patterns within the workloads. By providing a statistical characterization of these workload patterns, our study highlights the uniqueness of each pattern, paving the way for the development of realistic workload generation and resource provisioning techniques that can benefit a range of applications and research areas.

2024-10-05

2024 IEEE International Conference on Software Maintenance and Evolution (ICSME) (publié)

Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems

Altaf Allah Abbassi

Houssem Ben Braiek

Thomas Reid

2024-09-12

ArXiv (prépublication)

Quality issues in Machine Learning Software Systems

Pierre-Olivier Côté

Amin Nikanjam

Rached Bouchoucha

Ilan Basta

Mouna Abidi

Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are imp… (voir plus)lemented as software components and deployed in Machine Learning Software Systems (MLSSs). Problem: There is a strong need for ensuring the serving quality of MLSSs. False or poor decisions of such systems can lead to malfunction of other systems, significant financial losses, or even threat to human life. The quality assurance of MLSSs is considered as a challenging task and currently is a hot research topic. Moreover, it is important to cover all various aspects of the quality in MLSSs. Objective: This paper aims to investigate the characteristics of real quality issues in MLSSs from the viewpoint of practitioners. This empirical study aims to identify a catalog of bad-practices related to poor quality in MLSSs. Method: We plan to conduct a set of interviews with practitioners/experts, believing that interviews are the best method to retrieve their experience and practices when dealing with quality issues. We expect that the catalog of issues developed at this step will also help us later to identify the severity, root causes, and possible remedy for quality issues of MLSSs, allowing us to develop efficient quality assurance tools for ML models and MLSSs.

2024-09-10

Empirical Software Engineering (publié)

Reputation Gaming in Crowd Technical Knowledge Sharing

Iren Mazloomzadeh

Gias Uddin

Ashkan Sami

Stack Overflow incentive system awards users with reputation scores to ensure quality. The decentralized nature of the forum may make the in… (voir plus)centive system prone to manipulation. This paper offers, for the first time, a comprehensive study of the reported types of reputation manipulation scenarios that might be exercised in Stack Overflow and the prevalence of such reputation gamers by a qualitative study of 1,697 posts from meta Stack Exchange sites. We found four different types of reputation fraud scenarios, such as voting rings where communities form to upvote each other repeatedly on similar posts. We developed algorithms that enable platform managers to automatically identify these suspicious reputation gaming scenarios for review. The first algorithm identifies isolated/semi-isolated communities where probable reputation frauds may occur mostly by collaborating with each other. The second algorithm looks for sudden unusual big jumps in the reputation scores of users. We evaluated the performance of our algorithms by examining the reputation history dashboard of Stack Overflow users from the Stack Overflow website. We observed that around 60-80% of users flagged as suspicious by our algorithms experienced reductions in their reputation scores by Stack Overflow.

2024-09-03

ACM Transactions on Software Engineering and Methodology (publié)

Mining Action Rules for Defect Reduction Planning

Khouloud Oueslati

Gabriel Laberge

Maxime Lamothe

Defect reduction planning plays a vital role in enhancing software quality and minimizing software maintenance costs. By training a black bo… (voir plus)x machine learning model and "explaining" its predictions, explainable AI for software engineering aims to identify the code characteristics that impact maintenance risks. However, post-hoc explanations do not always faithfully reflect what the original model computes. In this paper, we introduce CounterACT, a Counterfactual ACTion rule mining approach that can generate defect reduction plans without black-box models. By leveraging action rules, CounterACT provides a course of action that can be considered as a counterfactual explanation for the class (e.g., buggy or not buggy) assigned to a piece of code. We compare the effectiveness of CounterACT with the original action rule mining algorithm and six established defect reduction approaches on 9 software projects. Our evaluation is based on (a) overlap scores between proposed code changes and actual developer modifications; (b) improvement scores in future releases; and (c) the precision, recall, and F1-score of the plans. Our results show that, compared to competing approaches, CounterACT's explainable plans achieve higher overlap scores at the release level (median 95%) and commit level (median 85.97%), and they offer better trade-off between precision and recall (median F1-score 88.12%). Finally, we venture beyond planning and explore leveraging Large Language models (LLM) for generating code edits from our generated plans. Our results show that suggested LLM code edits supported by our plans are actionable and are more likely to pass relevant test cases than vanilla LLM code recommendations.

2024-07-11

Proceedings of the ACM on Software Engineering (publié)

DeepCodeProbe: Towards Understanding What Models Trained on Code Learn

Vahid Majdinasab

Amin Nikanjam

2024-07-10

ArXiv (prépublication)