91 citations · 250 across the 37 of their papers we have counts for
38 papers
Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
Saeid Jamshidi, Amin Nikanjam, Arghavan Moradi Dakhel +2
Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses, making them vulnerable to prompt-injection and context-…
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…
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…
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…
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…
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…
Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
Vahid Majdinasab, Amin Nikanjam, Foutse Khomh
The rapid advancement of Large Language Models (LLMs) has outpaced traditional evaluation methods. Static benchmarks fail to capture the depth and breadth of LLM capabilities and e…
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things
Saeid Jamshidi, Amin Nikanjam, Nafi Kawser Wazed +1
As the Internet of Things (IoT) continues to expand, ensuring the security of connected devices has become increasingly critical. Traditional Intrusion Detection Systems (IDS) ofte…
A Taxonomy of Inefficiencies in LLM-Generated Python Code
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam +1
Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed LLM-generated code and identified various…
Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom, Leuson Da Silva, Amin Nikanjam +1
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based techniques show promising results by le…
An Efficient Model Maintenance Approach for MLOps
Forough Majidi, Foutse Khomh, Heng Li +1
In recent years, many industries have utilized machine learning (ML) models in their systems. Ideally, ML models should be trained on and applied to data from the same distribution…
Fault Localization in Deep Learning-based Software: A System-level Approach
Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh
Over the past decade, Deep Learning (DL) has become an integral part of our daily lives. This surge in DL usage has heightened the need for developing reliable DL software systems.…
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil +4
Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-ba…
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn
Vahid Majdinasab, Amin Nikanjam, Foutse Khomh
Machine learning models trained on code and related artifacts offer valuable support for software maintenance but suffer from interpretability issues due to their complex internal…
TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models
Florian Tambon, Amin Nikanjam, Cyrine Zid +2
Large Language Models (LLMs) excel in code-related tasks like code generation, but benchmark evaluations often overlook task characteristics, such as difficulty. Moreover, benchmar…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Bugs in Large Language Models Generated Code: An Empirical Study
Florian Tambon, Arghavan Moradi Dakhel, Amin Nikanjam +3
Large Language Models (LLMs) for code have gained significant attention recently. They can generate code in different programming languages based on provided prompts, fulfilling a…
Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
Vahid Majdinasab, Amin Nikanjam, Foutse Khomh
Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not contain code from protected sources. The rec…
Harnessing Pre-trained Generalist Agents for Software Engineering Tasks
Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh
Nowadays, we are witnessing an increasing adoption of Artificial Intelligence (AI) to develop techniques aimed at improving the reliability, effectiveness, and overall quality of s…
Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study
Mohammad Mehdi Morovati, Florian Tambon, Mina Taraghi +2
Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to produce intelligent agents. Despite rece…
Data Cleaning and Machine Learning: A Systematic Literature Review
Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed +2
Context: Machine Learning (ML) is integrated into a growing number of systems for various applications. Because the performance of an ML model is highly dependent on the quality of…
Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing
Arghavan Moradi Dakhel, Amin Nikanjam, Vahid Majdinasab +2
One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test gen…
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam +2
Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement learning, has shown significant potential in achieving human-level autonomy in a wide range of dom…
Bug Characterization in Machine Learning-based Systems
Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon +3
Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component…