172 citations · 802 across the 171 of their papers we have counts for
31 papers · 1 filter
Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems
Armstrong Foundjem, Lionel Nganyewou Tidjon, Leuson Da Silva +1
Machine learning (ML) underpins foundation models in finance, healthcare, and critical infrastructure, making them targets for data poisoning, model extraction, prompt injection, a…
QMon: Monitoring the Execution of Quantum Circuits with Mid-Circuit Measurement and Reset
Ning Ma, Jianjun Zhao, Foutse Khomh +2
Unlike classical software, where logging and runtime tracing can effectively reveal internal execution status, quantum circuits possess unique properties, such as the no-cloning th…
A Survey of Bugs in AI-Generated Code
Ruofan Gao, Amjed Tahir, Peng Liang +2
Developers are widely using AI code-generation models, aiming to increase productivity and efficiency. However, there are also quality concerns regarding the AI-generated code. The…
The Moral Consistency Pipeline: Continuous Ethical Evaluation for Large Language Models
Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel +2
The rapid advancement and adaptability of Large Language Models (LLMs) highlight the need for moral consistency, the capacity to maintain ethically coherent reasoning across varied…
Multi-LLM Energy Reasoning for Binary-Free Zero-Day IoT Detection
Saeid Jamshidi, Foutse Khomh, Kawser Wazed Nafi +2
Securing Internet of Things (IoT) firmware remains difficult due to proprietary binaries, stripped symbols, heterogeneous architectures, and limited access to executable code. Exis…
Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh
Despite their wide adoption in various domains (e.g., healthcare, finance, software engineering), Deep Learning (DL)-based applications suffer from many bugs, failures, and vulnera…
Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation
Saeid Jamshidi, Arghavan Moradi Dakhel, Kawser Wazed Nafi +1
The Model Context Protocol (MCP) enables Large Language Models (LLMs) to interact with external tools via tool descriptors, thereby extending their capabilities for task execution,…
Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway
Saeid Jamshidi, Amin Nikanjam, Negar Shahabi +4
As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computatio…
Green Deep Reinforcement Learning for IoT Edge Intrusion Detection
Saeid Jamshidi, Foutse Khomh, Rolando Herrero +2
The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity challenges, particularly in detecting and mitigating Distributed Denial-of-Service (DDoS) attacks…
RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring
Khouloud Oueslati, Maxime Lamothe, Foutse Khomh
Large Language Models (LLMs) have substantially influenced various software engineering tasks. Indeed, in the case of software refactoring, traditional LLMs have shown the ability…
Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs
Mina Taraghi, Yann Pequignot, Amin Nikanjam +2
Organizations increasingly adapt Large Language Models (LLMs) from public repositories such as HuggingFace to downstream tasks. Prior work shows that even fine-tuning on benign dat…
Refactoring with LLMs: Bridging Human Expertise and Machine Understanding
Yonnel Chen Kuang Piao, Jean Carlors Paul, Leuson Da Silva +3
Code refactoring is a fundamental software engineering practice aimed at improving code quality and maintainability. Despite its importance, developers often neglect refactoring du…
BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Yinghang Ma, Jiho Shin, Leuson Da Silva +5
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. However, these solutions are typically evalu…
Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications
Patrick Loic Foalem, Leuson Da Silva, Foutse Khomh +2
Machine learning (ML) is increasingly applied across industries to automate decision-making, but concerns about ethical and legal compliance remain due to limited transparency, fai…
FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
Moses Openja, Paolo Arcaini, Foutse Khomh +1
Deep neural networks (DNNs) are being utilized in various aspects of our daily lives, including high-stakes decision-making applications that impact individuals. However, these sys…
An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects
Kaveh Shahedi, Nana Gyambrah, Heng Li +2
Performance is a critical quality attribute in software development, yet the impact of method-level code changes on performance evolution remains poorly understood. While developer…
From Technical Excellence to Practical Adoption: Lessons Learned Building an ML-Enhanced Trace Analysis Tool
Kaveh Shahedi, Matthew Khouzam, Heng Li +2
System tracing has become essential for understanding complex software behavior in modern systems, yet sophisticated trace analysis tools face significant adoption gaps in industri…
ReCatcher: Towards LLMs Regression Testing for Code Generation
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam +1
Large Language Models (LLMs) for code generation evolve rapidly through fine-tuning, merging, or new model releases. However, such updates can introduce regressions, not only in co…
Adversarial Attack Classification and Robustness Testing for Large Language Models for Code
Yang Liu, Armstrong Foundjem, Foutse Khomh +1
Large Language Models (LLMs) have become vital tools in software development tasks such as code generation, completion, and analysis. As their integration into workflows deepens, e…
SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
Roozbeh Aghili, Xingfang Wu, Foutse Khomh +1
Software logs are messages recorded during the execution of a software system that provide crucial run-time information about events and activities. Although software logs have a c…
Performance Smells in ML and Non-ML Python Projects: A Comparative Study
François Belias, Leuson Da Silva, Foutse Khomh +1
Python is widely adopted across various domains, especially in Machine Learning (ML) and traditional software projects. Despite its versatility, Python is susceptible to performanc…
Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review
Saeid Jamshidi, Amin Nikanjam, Kawser Wazed Nafi +2
The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
Evaluating Machine Learning-Driven Intrusion Detection Systems in IoT: Performance and Energy Consumption
Saeid Jamshidi, Kawser Wazed Nafi, Amin Nikanjam +1
In the evolving landscape of the Internet of Things (IoT), Machine Learning (ML)-based Intrusion Detection Systems (IDS) represent a significant advancement, especially when integr…