91 citations · 127 across the 18 of their papers we have counts for
18 papers
Synthetic Semantic Supervision for Contrastive Code Representation Learning in Small Transformers: An Empirical Study
Kenneth Paulsen, Florian Tambon, Mike Papadakis +1
General-purpose code embeddings power tools for code search, classification, and retrieval. Compact transformer encoders for code typically rely on either human-written docstrings…
On the risk of coding before testing: An empirical study on LLM-based test generation workflow
Michael Konstantinou, Florian Tambon, Mike Papadakis
Large Language Models (LLMs) are increasingly used in software engineering workflows to generate both source code and test suites. This dual capability has enabled emerging develop…
Flow-A11y: Flow-Aware Accessibility Testing
Nasr Eddine Fliti, Leisan Kokorina, Florian Tambon +1
Modern web applications increasingly expose accessibility barriers through interaction flows rather than static page snapshots. Keyboard traps, focus loss, modal leakage, delayed s…
Prompt Coverage Adequacy
Florian Tambon, Michael Konstantinou, Cedric Richter +3
In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software development by shifting the f…
Inferring Code Correctness from Specification
Tambon Florian, Papadakis Mike
Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale. However, validating the correctness of LLM-generated…
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Mohamed Djilani, Thibault Simonetto, Karim Tit +4
Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, thei…
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…
Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs
Sylvain Kouemo Ngassom, Arghavan Moradi Dakhel, Florian Tambon +1
LLM-based assistants, such as GitHub Copilot and ChatGPT, have the potential to generate code that fulfills a programming task described in a natural language description, referred…
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…
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
Mina Taraghi, Gianolli Dorcelus, Armstrong Foundjem +2
The ubiquity of large-scale Pre-Trained Models (PTMs) is on the rise, sparking interest in model hubs, and dedicated platforms for hosting PTMs. Despite this trend, a comprehensive…
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon, Foutse Khomh, Giuliano Antoniol
To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods for test case generation techniques are being developed. When confronted…
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…
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…
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
Florian Tambon, Vahid Majdinasab, Amin Nikanjam +2
Testing Deep Learning (DL) systems is a complex task as they do not behave like traditional systems would, notably because of their stochastic nature. Nonetheless, being able to ad…
A Probabilistic Framework for Mutation Testing in Deep Neural Networks
Florian Tambon, Foutse Khomh, Giuliano Antoniol
Context: Mutation Testing (MT) is an important tool in traditional Software Engineering (SE) white-box testing. It aims to artificially inject faults in a system to evaluate a test…
Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow
Florian Tambon, Amin Nikanjam, Le An +2
Deep Learning (DL) frameworks are now widely used, simplifying the creation of complex models as well as their integration to various applications even to non DL experts. However,…
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Florian Tambon, Gabriel Laberge, Le An +7
Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aer…
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks
Florian Tambon, Giulio Antoniol, Foutse Khomh
Deep Neural Networks (DNN) applications are increasingly becoming a part of our everyday life, from medical applications to autonomous cars. Traditional validation of DNN relies on…