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

Nanda Assobjio Brice Yvan

Maîtrise recherche - Polytechnique

Ahmed Haj Yahmed

Maîtrise recherche - Polytechnique

Gabriel Laberge

Doctorat - Polytechnique

forough majidi

Doctorat - Polytechnique

Site web

mohammadhossein.malekpour@gmail.com

Mohammadhossein Malekpour

Maîtrise recherche - Polytechnique

Elnathan Tiokou Tiokou Fangang

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Arghavan Moradi Dakhel

Postdoctorat - Polytechnique

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

Publications

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 (publié)

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… (voir plus)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 (prépublication)

Detection and evaluation of bias-inducing features in machine learning

Moses Openja

gabriel laberge

2023-10-19

ArXiv (prépublication)

Studying the characteristics of AIOps projects on GitHub

Roozbeh Aghili

Heng Li

2023-10-18

Empirical Software Engineering (publié)

Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)

Matteo Biagiola

Nicolás Cardozo

Donghwan Shin

Andrea Stocco

Vincenzo Riccio

2023-10-13

ACM SIGSOFT Softw. Eng. Notes (publié)

On the effectiveness of log representation for log-based anomaly detection

Xingfang Wu

Heng Li

2023-10-09

Empirical Software Engineering (publié)

Data Cleaning and Machine Learning: A Systematic Literature Review

Pierre-Olivier Cot'e

Amin Nikanjam

Nafisa Ahmed

Dmytro Humeniuk

Context: Machine Learning (ML) is integrated into a growing number of systems for various applications. Because the performance of an ML mod… (voir plus)el is highly dependent on the quality of the data it has been trained on, there is a growing interest in approaches to detect and repair data errors (i.e., data cleaning). Researchers are also exploring how ML can be used for data cleaning; hence creating a dual relationship between ML and data cleaning. To the best of our knowledge, there is no study that comprehensively reviews this relationship. Objective: This paper's objectives are twofold. First, it aims to summarize the latest approaches for data cleaning for ML and ML for data cleaning. Second, it provides future work recommendations. Method: We conduct a systematic literature review of the papers published between 2016 and 2022 inclusively. We identify different types of data cleaning activities with and for ML: feature cleaning, label cleaning, entity matching, outlier detection, imputation, and holistic data cleaning. Results: We summarize the content of 101 papers covering various data cleaning activities and provide 24 future work recommendations. Our review highlights many promising data cleaning techniques that can be further extended. Conclusion: We believe that our review of the literature will help the community develop better approaches to clean data.

2023-10-03

ArXiv (prépublication)

Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges

Ahmed Haj Yahmed

Altaf Allah Abbassi

Amin Nikanjam

Heng Li

Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement learning, has shown significant potential in achieving hum… (voir plus)an-level autonomy in a wide range of domains, including robotics, computer vision, and computer games. This potential justifies the enthusiasm and growing interest in DRL in both academia and industry. However, the community currently focuses mostly on the development phase of DRL systems, with little attention devoted to DRL deployment. In this paper, we propose an empirical study on Stack Overflow (SO), the most popular Q&A forum for developers, to uncover and understand the challenges practitioners faced when deploying DRL systems. Specifically, we categorized relevant SO posts by deployment platforms: server/cloud, mobile/embedded system, browser, and game engine. After filtering and manual analysis, we examined 357 SO posts about DRL deployment, investigated the current state, and identified the challenges related to deploying DRL systems. Then, we investigate the prevalence and difficulty of these challenges. Results show that the general interest in DRL deployment is growing, confirming the study’s relevance and importance. Results also show that DRL deployment is more difficult than other DRL issues. Additionally, we built a taxonomy of 31 unique challenges in deploying DRL to different platforms. On all platforms, RL environment-related challenges are the most popular, and communication-related challenges are the most difficult among practitioners. We hope our study inspires future research and helps the community overcome the most common and difficult challenges practitioners face when deploying DRL systems.

2023-10-01

2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) (publié)

An Empirical Study on Bugs Inside PyTorch: A Replication Study

Vahid Majdinasab

Sharon Chee Yin Ho

Mohayeminul Islam

Diego Elias Costa

Emad Shihab

Sarah Nadi

Muhammad Raza

Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patter… (voir plus)ns and powering intelligent behaviour. A core enabler of this change in software development is the availability of easy-to-use deep learning libraries. Libraries like PyTorch and TensorFlow empower a large variety of intelligent systems, offering a multitude of algorithms and configuration options, applicable to numerous domains of systems. However, bugs in those popular deep learning libraries also may have dire consequences for the quality of systems they enable; thus, it is important to understand how bugs are identified and fixed in those libraries.Inspired by a study of Jia et al., which investigates the bug identification and fixing process at TensorFlow, we characterize bugs in the PyTorch library, a very popular deep learning framework. We investigate the causes and symptoms of bugs identified during PyTorch’s development, and assess their locality within the project, and extract patterns of bug fixes. Our results highlight that PyTorch bugs are more like traditional software projects bugs, than related to deep learning characteristics. Finally, we also compare our results with the study on TensorFlow, highlighting similarities and differences across the bug identification and fixing process.

2023-10-01

2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) (publié)

Robustness assessment of hyperspectral image CNNs using metamorphic testing

Rached Bouchoucha

Houssem Ben Braiek

Sonia Bouzidi

Rania Zaatour

2023-10-01

Information and Software Technology (publié)

Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

Arghavan Moradi Dakhel

Amin Nikanjam

Vahid Majdinasab

Michel C. Desmarais

One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenan… (voir plus)ce costs. The goal of automated test generation tools is to ease the development of tests by suggesting efficient bug-revealing tests. Recently, researchers have leveraged Large Language Models (LLMs) of code to generate unit tests. While the code coverage of generated tests was usually assessed, the literature has acknowledged that the coverage is weakly correlated with the efficiency of tests in bug detection. To improve over this limitation, in this paper, we introduce MuTAP for improving the effectiveness of test cases generated by LLMs in terms of revealing bugs by leveraging mutation testing. Our goal is achieved by augmenting prompts with surviving mutants, as those mutants highlight the limitations of test cases in detecting bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate their performance on different benchmarks. Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets. Among these, 17% remained undetected by both the current state-of-the-art fully automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57% on synthetic buggy code, outperforming all other approaches in our evaluation. Our findings suggest that although LLMs can serve as a useful tool to generate test cases, they require specific post-processing steps to enhance the effectiveness of the generated test cases which may suffer from syntactic or functional errors and may be ineffective in detecting certain types of bugs and testing corner cases PUTs.

2023-08-31

ArXiv (prépublication)

A comparison of reinforcement learning frameworks for software testing tasks

Paulina Stevia Nouwou Mindom

Amin Nikanjam

2023-08-24

Empirical Software Engineering (publié)