Foutse Khomh

houssem.ben-braiek@polymtl.ca

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

forough.majidi@mila.quebec

Site web

anthonia-oluchukwu.njoku@mila.quebec

Anthonia Oluchukwu Njoku

Doctorat - Polytechnique

khouloud.oueslati@mila.quebec

Khouloud Oueslati

Maîtrise recherche - Polytechnique

Arian Qazvini

Maîtrise recherche - Polytechnique

arian.qazvini@mila.quebec

Site web

Github

Elnathan Tiokou Tiokou Fangang

Maîtrise recherche - Polytechnique

elnathan.tiokou@mila.quebec

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

yasmine.ben-braiek@mila.quebec

Publications

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)

Data-access performance anti-patterns in data-intensive systems

Biruk Asmare Muse

Kawser Wazed Nafi

Giuliano Antoniol

Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional softwar… (voir plus)e, data-intensive systems are prone to technical debts introduced to cope-up with the pressure of time and resource constraints on developers. Data-access is a critical component of data-intensive systems as it determines the overall performance and functionality of such systems. While data access technical debts are getting attention from the research community, technical debts affecting the performance, are not well investigated. Objective: Identify, categorize, and validate data access performance issues in the context of NoSQL-based and polyglot persistence data-intensive systems using qualitative study. Method: We collect issues from NoSQL-based and polyglot persistence open-source data-intensive systems and identify data access performance issues using inductive coding and build a taxonomy of the root causes. Then, we validate the perceived relevance of the newly identified performance issues using a developer survey.

2024-08-28

Empirical Software Engineering (publié)

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… (voir plus)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.

2024-07-26

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)

Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs

Sylvain Kouemo Ngassom

Arghavan Moradi Dakhel

Florian Tambon

LLM-based assistants, such as GitHub Copilot and ChatGPT, have the potential to generate code that fulfills a programming task described in … (voir plus)a natural language description, referred to as a prompt. The widespread accessibility of these assistants enables users with diverse backgrounds to generate code and integrate it into software projects. However, studies show that code generated by LLMs is prone to bugs and may miss various corner cases in task specifications. Presenting such buggy code to users can impact their reliability and trust in LLM-based assistants. Moreover, significant efforts are required by the user to detect and repair any bug present in the code, especially if no test cases are available. In this study, we propose a self-refinement method aimed at improving the reliability of code generated by LLMs by minimizing the number of bugs before execution, without human intervention, and in the absence of test cases. Our approach is based on targeted Verification Questions (VQs) to identify potential bugs within the initial code. These VQs target various nodes within the Abstract Syntax Tree (AST) of the initial code, which have the potential to trigger specific types of bug patterns commonly found in LLM-generated code. Finally, our method attempts to repair these potential bugs by re-prompting the LLM with the targeted VQs and the initial code. Our evaluation, based on programming tasks in the CoderEval dataset, demonstrates that our proposed method outperforms state-of-the-art methods by decreasing the number of targeted errors in the code between 21% to 62% and improving the number of executable code instances to 13%.

2024-07-09

Proceedings of the 1st ACM International Conference on AI-Powered Software (publié)

Design smells in multi-language systems and bug-proneness: a survival analysis

Mouna Abidi

Md Saidur Rahman

Moses Openja

2024-07-02

Empirical Software Engineering (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.

2024-06-30

Information and Software Technology (publié)

A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4 code interpreter

Mohamed Salah Bouafif

Chen Zheng

Ilham Qasse

Ed Zulkoski

Mohammad Hamdaqa

The surge in the adoption of smart contracts necessitates rigorous auditing to ensure their security and reliability. Manual auditing, altho… (voir plus)ugh comprehensive, is time-consuming and heavily reliant on the auditor's expertise. With the rise of Large Language Models (LLMs), there is growing interest in leveraging them to assist auditors in the auditing process (co-auditing). However, the effectiveness of LLMs in smart contract co-auditing is contingent upon the design of the input prompts, especially in terms of context description and code length. This paper introduces a novel context-driven prompting technique for smart contract co-auditing. Our approach employs three techniques for context scoping and augmentation, encompassing code scoping to chunk long code into self-contained code segments based on code inter-dependencies, assessment scoping to enhance context description based on the target assessment goal, thereby limiting the search space, and reporting scoping to force a specific format for the generated response. Through empirical evaluations on publicly available vulnerable contracts, our method demonstrated a detection rate of 96\% for vulnerable functions, outperforming the native prompting approach, which detected only 53\%. To assess the reliability of our prompting approach, manual analysis of the results was conducted by expert auditors from our partner, Quantstamp, a world-leading smart contract auditing company. The experts' analysis indicates that, in unlabeled datasets, our proposed approach enhances the proficiency of the GPT-4 code interpreter in detecting vulnerabilities.

2024-06-25

ArXiv (prépublication)

GIST: Generated Inputs Sets Transferability in Deep Learning

Florian Tambon

Giuliano Antoniol

2024-06-12

ACM Transactions on Software Engineering and Methodology (publié)