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

Gabriel Laberge

Doctorat - Polytechnique

Github

forough majidi

Doctorat - Polytechnique

Site web

mohammadhossein.malekpour@gmail.com

Mo Malekpour

Maîtrise recherche - Polytechnique

Site web

Github

Elnathan Tiokou Tiokou Fangang

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Arghavan Moradi Dakhel

Postdoctorat - Polytechnique

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

Github

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

Publications

LIBS-Raman Multimodal Architecture for Automated Lunar Prospecting

Jérôme Pigeon

Richard Boudreault

Ahmed Ashraf

Pooneh Maghoul

2024-10-10

Earth and Space 2024 (publié)

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer

Rached Bouchoucha

Ahmed Haj Yahmed

Darshan Patil

Janarthanan Rajendran

Amin Nikanjam

Sarath Chandar

Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However… (voir plus), like any other software system, DRL-based software systems are susceptible to faults that pose unique challenges for debugging and diagnosing. These faults often result in unexpected behavior without explicit failures and error messages, making debugging difficult and time-consuming. Therefore, automating the monitoring and diagnosis of DRL systems is crucial to alleviate the burden on developers. In this paper, we propose RLExplorer, the first fault diagnosis approach for DRL-based software systems. RLExplorer automatically monitors training traces and runs diagnosis routines based on properties of the DRL learning dynamics to detect the occurrence of DRL-specific faults. It then logs the results of these diagnoses as warnings that cover theoretical concepts, recommended practices, and potential solutions to the identified faults. We conducted two sets of evaluations to assess RLExplorer. Our first evaluation of faulty DRL samples from Stack Overflow revealed that our approach can effectively diagnose real faults in 83% of the cases. Our second evaluation of RLExplorer with 15 DRL experts/developers showed that (1) RLExplorer could identify 3.6 times more defects than manual debugging and (2) RLExplorer is easily integrated into DRL applications.

2024-10-06

ArXiv (prépublication)

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer

Rached Bouchoucha

Ahmed Haj Yahmed

Darshan Patil

Janarthanan Rajendran

Amin Nikanjam

Sarath Chandar

2024-10-06

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

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

Roozbeh Aghili

Qiaolin Qin

Heng Li

2024-10-06

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

What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach

Xingfang Wu

Heng Li

Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications … (voir plus)and systems. State-of-the-art log-based anomaly detection approaches typically leverage deep learning models to capture the semantic or sequential information in the log data and detect anomalous runtime behaviors. However, the impacts of these different types of information are not clear. In addition, existing approaches have not captured the timestamps in the log data, which can potentially provide more fine-grained temporal information than sequential information. In this work, we propose a configurable transformer-based anomaly detection model that can capture the semantic, sequential, and temporal information in the log data and allows us to configure the different types of information as the model's features. Additionally, we train and evaluate the proposed model using log sequences of different lengths, thus overcoming the constraint of existing methods that rely on fixed-length or time-windowed log sequences as inputs. With the proposed model, we conduct a series of experiments with different combinations of input features to evaluate the roles of different types of information in anomaly detection. When presented with log sequences of varying lengths, the model can attain competitive and consistently stable performance compared to the baselines. The results indicate that the event occurrence information plays a key role in identifying anomalies, while the impact of the sequential and temporal information is not significant for anomaly detection in the studied public datasets. On the other hand, the findings also reveal the simplicity of the studied public datasets and highlight the importance of constructing new datasets that contain different types of anomalies to better evaluate the performance of anomaly detection models.

2024-09-30

ArXiv (prépublication)

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.

2024-09-18

ArXiv (prépublication)

An Empirical Study of Sensitive Information in Logs

Roozbeh Aghili

Heng Li

2024-09-17

ArXiv (prépublication)

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

Altaf Allah Abbassi

Houssem Ben Braiek

Thomas Reid

2024-09-13

ArXiv (prépublication)

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.

2024-09-04

ACM Transactions on Software Engineering and Methodology (publié)

Assessing Programming Task Difficulty for Efficient Evaluation of Large Language Models

Florian Tambon

Amin Nikanjam

Giuliano Antoniol

2024-07-30

ArXiv (prépublication)

TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models

Florian Tambon

Amin Nikanjam

Cyrine Zid

Giuliano Antoniol

2024-07-30

ArXiv (prépublication)

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.

2024-07-12

Proceedings of the ACM on Software Engineering (publié)