Foutse Khomh

houssem.ben-braiek@polymtl.ca

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

Houssem Ben Braiek

Postdoctorate - Polytechnique Montréal

Website

Github

anthonia-oluchukwu.njoku@mila.quebec

Anthonia Oluchukwu Njoku

PhD - Polytechnique Montréal

khouloud.oueslati@mila.quebec

Khouloud Oueslati

Master's Research - Polytechnique Montréal

Arian Qazvini

Master's Research - Polytechnique Montréal

arian.qazvini@mila.quebec

Website

Github

Elnathan Tiokou Tiokou Fangang

Master's Research - Polytechnique Montréal

elnathan.tiokou@mila.quebec

Publications

Semantic Dynamic Time Warping for Condition-based Health Monitoring and Explainable Anomaly Detection in Dynamic Cyber–Physical Systems

Lamyaa Elmahi

Hugo Gagnon

Soumaya Yacout

Dynamic cyber–physical systems exhibit dynamic normal behaviour driven by operating conditions, human input, and subsystem interactions, c… (see more)ausing operational cycles to vary in duration and multivariate time-series trajectories. This challenges anomaly detection because a single normal baseline is unidentifiable from telemetry, and discretizing behaviour into a finite set of modes is insufficient. We propose Semantic Dynamic Time Warping (SDTW), an elastic cycle-to-cycle distance that aligns cycles in a semantic descriptor space. For anomaly detection, SDTW is paired with a k-nearest-neighbour scoring rule using a fixed normal reference library. We perform aircraft-disjoint hyperparameter selection and held-out testing, and quantify uncertainty using an aircraft-level bootstrap. On held-out aircraft, SDTW improves aircraft-balanced PR-AUC and ROC-AUC over the strongest metric-specific baselines by 32.3% and 12.7%, respectively, with 95% bootstrap paired-delta confidence intervals strictly above zero for both improvements. SDTW provides explanations by decomposing alignment mismatches into ranked anomalous intervals, signals, and directed signal relations. Cross-domain generalizability was evaluated on the public NASA C-MAPSS run-to-failure benchmark using the aircraft-selected hyperparameter configuration unchanged. On the single-operating-condition subsets, SDTW belonged to the statistically indistinguishable top-performing group, while on both six-operating-condition subsets, it outperformed every baseline. Finally, an aircraft Shutoff Valve Current case study demonstrates SDTW for similarity-based diagnosis by retrieval. SDTW achieved higher retrieval quality than Classical Multivariate DTW and an internal similarity metric, including the retrieval of waveform-dissimilar cycles satisfying the same failure-mode definition. This indicates that SDTW can retrieve operationally similar examples even when their waveforms differ as similarity is driven by time-local inter-variable dependencies.

2026-11-30

Computers & Industrial Engineering (published)

GrayShield: Bit-Level Sanitization for Transformer Model Supply-Chain Security

Armstrong Foundjem

Tsung-Hsien Chuang

Mohamed Amine Merzouk

Transformer models such as BERT and Vision Transformer~(ViT) achieve strong performance via densely parameterized attention backbones. Howev… (see more)er, the least significant bits~(LSBs) of their 32-bit floating-point weights can be abused as covert channels to conceal malicious payloads, posing a serious threat to the AI model supply chain. We propose \GS (\GSabbr), a lightweight, post-training, zero-data sanitization method that completely replaces the declared mantissa-LSB channel with a Gray-code-guided low-transition sequence. Complete payload-independent overwrite, whether keyed or public, makes the sanitized target bits independent of the embedded payload and gives that declared channel zero capacity. Gray coding supplies overwrite structure, while a keyed per-tensor phase supplies pattern diversity. Benchmarked against seven post-training defenses on four Transformer model presets and two real-world malware payloads, \GSabbr maintains sub-

2026-10-02

arXiv (preprint)

AgentInspect: Diagnosing Behavioral Failures in Artificial Intelligence Agents

Ruchira Manke

Mohammad Wardat

Hridesh Rajan

Effectively testing Artificial Intelligence (AI) agents remains a fundamental challenge due to their stochastic reasoning, vast and diverse … (see more)input space, reliance on external tools, and operation in dynamic execution environments; factors that demand new testing methodologies explicitly tailored to the complex and interactive nature of agent-based systems. This work presents a novel methodology for testing AI agents, with a particular focus on assessing their behavioral robustness under varied operational conditions. Our approach relies on following key technical innovations: (1) a coverage-guided test input generation strategy based on agent- specific coverage objectives, (2) a capture-and-simulate mechanism that systematically emulates abnormal tool behaviors to mimic real-world execution failures, and (3) a deterministic behavioral failure detection approach that enables consistent identification of failures across different test inputs. We developed AgentInspect, a framework that automatically detects six types of behavioral failures in LangChain-based AI agents by analyzing their execution trajectories across three evaluation settings: a baseline setting using real tool responses, a simulated setting incorporating synthetic tool responses, and a hybrid setting that combines the real and simulated tool responses. To evaluate our approach, we curated a benchmark of 35 AI agents obtained from GitHub. Our results show that AgentInspect consistently identifies different behavioral failures with high precision and recall across all three execution settings. In particular, the simulated and hybrid settings expose failure modes that do not emerge during baseline execution with real tool responses, thereby enabling a more comprehensive assessment of agent robustness. Our findings highlight AgentInspect’s effectiveness in revealing critical failures and its practical utility for systematic robustness evaluation of AI agents.

2026-09-30

Proceedings of the ACM on Software Engineering (published)

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy

Mina Taraghi

Mohammad Mehdi Morovati

The rapid adoption of foundation models has significantly expanded the capabilities of software systems, enabling them to perform complex la… (see more)nguage, reasoning, and interaction tasks that were previously difficult to automate. However, this progress has also introduced novel challenges that were largely absent in previous generations of software. In particular, the increasing integration of foundation models with external tools and resources raises new concerns regarding reliability, security, and robustness. The Model Context Protocol (MCP) has recently been proposed to standardize interactions between AI-based software systems, software tools, and external resources. Despite its growing adoption, there remains limited systematic understanding of real-world faults in MCP-based software systems. In this paper, we present the first large-scale taxonomy of faults in MCP servers, comprising five high-level fault categories derived from empirical evidence. To evaluate the completeness and generalizability of this taxonomy, we conduct a survey of MCP practitioners representing diverse professional roles. The results confirm that all MCP-specific fault categories occur in practice and reveal distinct characteristics that differentiate MCP-specific faults from non-MCP faults. Overall, these findings provide actionable guidance for building more robust MCP-based systems through boundary-aware testing, response validation, configuration diagnostics, and reproducible issue reporting.

2026-09-29

ACM Transactions on Software Engineering and Methodology (published)

Discovering Performance Archetypes: Critical-Path-Aware Pattern Analysis and Regression Detection

Kaveh Shahedi

Heng Li

Maxime Lamothe

Software performance analysis and prediction requires integrating multiple signals, as code structure alone cannot capture runtime behavior … (see more)shaped by execution frequency, resource contention, and I/O patterns. We present a critical-path-aware performance analysis methodology that automatically discovers recurring performance patterns by synthesizing static code features, dynamic execution traces, and kernel-level resource data. In a preliminary study across six real-world C/C++ applications (SQLite, OpenSSL, Zstandard, FFmpeg, cURL, and jq), we first empirically confirm that static complexity metrics explain only 10.4% of the variance (

2026-09-14

arXiv (preprint)

Revisiting Feedback-Driven LLM Code Repair: A Replication and Exploratory Java Extension

Louis Lalonde

Wassim Keddache

Thomas Perron Touchette

Leuson Da Silva

Since the advent of Large Language Models (LLMs), practitioners have increasingly leveraged them to support their software engineering tasks… (see more), including automated code repair, showing promising results. Yet, concerns regarding reproducibility and generalizability remain largely unexplored. To further evaluate these concerns and associated impacts, we partially reproduce and conduct an exploratory Java extension of the FeedbackEval benchmark [1], which evaluates how LLMs leverage different feedback types for Python code repair. First, we partially replicate the original study on 394 repair tasks using GPT-4o and Claude 3.5 Sonnet, reproducing and observing the main qualitative trends reported in the original work. Second, we conduct an exploratory Java extension by constructing 100 erroneous repair instances from 50 Java tasks and evaluating feedback effectiveness. Our results show that previous conclusions from Python may be sensitive to benchmark construction, feedback representation, and tooling ecosystem, motivating more controlled multilingual benchmarks. Specifically, while test feedback remains the strongest feedback type in our Python replication, the same ranking is not observed in our Java extension, as simple and JUnit-based test feedback do not differ significantly. We hypothesize that differences in feedback informativeness and tooling ecosystems, such as the verbosity of test frameworks, may partly explain such a difference. Finally, lighter prompts reduce cost without significant differences in repair effectiveness. Overall, our findings confirm key trends under a partially controlled replication and highlight the need for more rigorous multilingual evaluation and careful feedback design in LLMbased repair systems.

2026-08-30

arXiv (preprint)

The relationship between different python argument-passing mechanisms and fixes: An empirical study

Cyrine Zid

Massimiliano Di Penta

Giuliano Antoniol

2026-08-19

Empirical Software Engineering (published)

LLM Serving in the Wild: An Empirical Study of Frameworks, Methods, and System Designs

forough majidi

Mohammad Mehdi Morovati

Heng Li

Large Language Models (LLMs) are integrated into software systems and AI services, making efficient LLM serving a concern for software engin… (see more)eering. Serving LLMs is challenging because inference requires computation, memory, GPU resources, and execution while maintaining latency and throughput. Although prior research has proposed LLM inference, optimization, and serving techniques and frameworks, little is known about how they are adopted in practice. In this study, we investigate the use of LLM serving frameworks and serving methods in open-source software systems. We identify and analyze five LLM-specific frameworks: vLLM, SGLang, TensorRT-LLM, LMDeploy, and FlashInfer. We examine how these frameworks and techniques are adopted individually and in combination, how adoption varies across categories of LLMs, and how repositories differ in intent, focus, use case, and architectural design. Our results show that vLLM is the most visible framework in popularity and adoption, while parallel computation, memory management, and network pruning are the most frequently used serving-method categories. Multi-framework usage is limited, suggesting that developers rely on a single serving framework; however, combined frameworks connect complementary capabilities across the serving stack. Framework adoption varies across model families, modalities, model sizes, domain specializations, and deployment settings. Repository-level analysis shows that LLM serving frameworks support applications and architectures, including Reinforcement Learning (RL)-based reasoning, multimodal generation and understanding, microservices, and cloud infrastructure. Overall, this study provides a large-scale empirical characterization of LLM serving framework adoption in practice and offers insights for researchers, framework maintainers, and practitioners working on LLM systems.

2026-08-03

arXiv (preprint)

Notes about Human Body System & Nutrition

Nonvikan Karl-Augustt Alahassa

Bidossessi R.U. Alahassa

Julie Carrier

Nathalie Lacelle

Samuel Bassetto

Leonard Wantchekon

Mylène Bédard

Emmanuel Stip

Bruno Rémillard

Joel TOSSA

Suljo Linic

Bakary Manga

Jérôme Théau

Victor M. Panaretos

Damien Échevin

Mickaël Germain

David Haziza

Daniel F. Nadeau

Marlène Frigon … (see 14 more)

Christiane Rousseau

Andrew Granville

Maciej Augustyniak

Dimitrios Koukoulopoulos

Élise Vandomme

Wilfrid Gangbo

Raphael R. Kelani

J. B. Chabi Orou

Cyriaque Atindogbé

Mouloud-Beallah Belbahri

Jörg Matthes

Paresh Wankhade

Corine Bitossessi Laurenda Sinsin

Roseline Olory Agomma

We are studying the Human Body as a System with its Nutrition Requirement as energy pre-requisites & necessities. We find out that it is pos… (see more)sible to submit our daily nutrition requirements to some restaurants & Jounals : this practice is called Dietary Requests with Accomodations.

2026-07-27

Zenodo (unknown)

How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests

Iren Mazloomzadeh

Mohammad Mehdi Morovati

Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) c… (see more)oding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.

2026-07-22

arXiv (preprint)

Equivalent Numeric Approved Identity of Passport and Immigration Papers -- The Next Generation Identification Program (NGI) [A Short Introduction]

Nonvikan Karl-Augustt Alahassa

Bidossessi R.U. Alahassa

Julie Carrier

Joel TOSSA

Wilfrid Gangbo

Jörg Matthes

Cyriaque Atindogbé

Marlène Frigon

Christiane Rousseau

Maciej Augustyniak

Dimitrios Koukopoulos

Samuel Bassetto

J. B. Chabi Orou

Jérôme Théau

Damien Échevin

Raphael R. Kelani

David Haziza

Emmanuel Stip

Mylène Bédard … (see 8 more)

Élise Vandomme

Mouloud-Beallah Belbahri

Bakary Manga

Daniel F. Nadeau

Suljo Linic

Bruno Rémillard

Victor M. Panaretos

Nathalie Lacelle

We express an interest to Equivalent Numeric Approved Identity of Passport and Immigration Papers. [We are making a submission to the Journa… (see more)l of Banking and Financial Technology].

2026-07-17

Zenodo (unknown)

Memorandum, Notes on Visitations and Ongoing Projects in Canada (Version -1)

Nonvikan Karl-Augustt Alahassa

Bidossessi R.U. Alahassa

Julie Carrier

Nathalie Lacelle

Samuel Bassetto

Leonard Wantchekon

Mylène Bédard

Emmanuel Stip

Bruno Rémillard

Suljo Linic

J. Tossa

Bakary Manga

Jérôme Théau

Victor M. Panaretos

David Haziza

Daniel F. Nadeau

Marlène Frigon

Christiane Rousseau

Maciej Augustyniak … (see 8 more)

Dimitrios Koukoulopoulos

Vandomme, Élise

Gangbo, Wilfrid

Kelani, Raphael

Chabi Orou, Jean Bio

ATINDOGBE, Comlan Cyriaque

Wilfrid Gangbo

Raphael Kelani

We are on the Refugee Claimant list in Canada. We would like to express few notes of Memorandum.

2026-07-17

Zenodo (unknown)