187 citations · 278 across the 29 of their papers we have counts for
31 papers
From Failing to Passing: Evolving Natural Language Prompt Optimization Rules for LLM Code Generation
Amal Akli, Melissa Akli, Cedric Richter +2
Large language models are known to be sensitive to prompt formulation. Even minor variations in wording can substantially degrade performance. This sensitivity reveals an opportuni…
Federated Imputation under Heterogeneous Feature Spaces
Imane Hocine, Chaimaa Medjadji, Sylvain Kubler +2
Federated Learning (FL) enables collaborative training across decentralized clients, but most methods assume aligned feature schemas, an assumption that rarely holds in tabular set…
Centralized vs Decentralized Federated Learning: A trade-off performance analysis
Chaimaa Medjadji, Guilain Leduc, Sylvain Kubler +1
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data privacy especially with the huge…
Robustness Analysis of AI Models in Critical Energy Systems
Pantelis Dogoulis, Matthieu Jimenez, Salah Ghamizi +2
This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the security criterion. While these models perform well in regular grid…
GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding
Zeming Dong, Qiang Hu, Xiaofei Xie +4
Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code dat…
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data
Thibault Simonetto, Salah Ghamizi, Antoine Desjardins +2
State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings. However, the robustness of these models…
Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data. To alleviate the labeling effort, multiple test s…
CodeLens: An Interactive Tool for Visualizing Code Representations
Yuejun Guo, Seifeddine Bettaieb, Qiang Hu +2
Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing…
Distribution-aware Fairness Test Generation
Sai Sathiesh Rajan, Ezekiel Soremekun, Yves Le Traon +1
Ensuring that all classes of objects are detected with equal accuracy is essential in AI systems. For instance, being unable to identify any one class of objects could have fatal c…
ACoRe: Automated Goal-Conflict Resolution
Luiz Carvalho, Renzo Degiovanni, Matìas Brizzio +4
System goals are the statements that, in the context of software requirements specification, capture how the software should behave. Many times, the understanding of stakeholders o…
The Importance of Discerning Flaky from Fault-triggering Test Failures: A Case Study on the Chromium CI
Guillaume Haben, Sarra Habchi, Mike Papadakis +2
Flaky tests are tests that pass and fail on different executions of the same version of a program under test. They waste valuable developer time by making developers investigate fa…
Efficient Mutation Testing via Pre-Trained Language Models
Ahmed Khanfir, Renzo Degiovanni, Mike Papadakis +1
Mutation testing is an established fault-based testing technique. It operates by seeding faults into the programs under test and asking developers to write tests that reveal these…
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices
Salah Ghamizi, Maxime Cordy, Michail Papadakis +1
Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet…
Predicting Flaky Tests Categories using Few-Shot Learning
Amal Akli, Guillaume Haben, Sarra Habchi +2
Flaky tests are tests that yield different outcomes when run on the same version of a program. This non-deterministic behaviour plagues continuous integration with false signals, w…
An In-depth Study of Java Deserialization Remote-Code Execution Exploits and Vulnerabilities
Imen Sayar, Alexandre Bartel, Eric Bodden +1
Nowadays, an increasing number of applications uses deserialization. This technique, based on rebuilding the instance of objects from serialized byte streams, can be dangerous sinc…
CodeBERT-nt: code naturalness via CodeBERT
Ahmed Khanfir, Matthieu Jimenez, Mike Papadakis +1
Much of software-engineering research relies on the naturalness of code, the fact that code, in small code snippets, is repetitive and can be predicted using statistical language m…
What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness
Sarra Habchi, Guillaume Haben, Jeongju Sohn +4
Flaky tests are defined as tests that manifest non-deterministic behaviour by passing and failing intermittently for the same version of the code. These tests cripple continuous in…
Software Fairness: An Analysis and Survey
Ezekiel Soremekun, Mike Papadakis, Maxime Cordy +1
In the last decade, researchers have studied fairness as a software property. In particular, how to engineer fair software systems? This includes specifying, designing, and validat…
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
Qiang Hu, Yuejun Guo, Maxime Cordy +3
Applying deep learning to science is a new trend in recent years which leads DL engineering to become an important problem. Although training data preparation, model architecture d…
Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution
Milos Ojdanic, Ezekiel Soremekun, Renzo Degiovanni +2
When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. We conduct an expl…
A Qualitative Study on the Sources, Impacts, and Mitigation Strategies of Flaky Tests
Sarra Habchi, Guillaume Haben, Mike Papadakis +2
Test flakiness forms a major testing concern. Flaky tests manifest non-deterministic outcomes that cripple continuous integration and lead developers to investigate false alerts. I…
Smells in System User Interactive Tests
Renaud Rwemalika, Sarra Habchi, Mike Papadakis +2
Test smells are known as bad development practices that reflect poor design and implementation choices in software tests. Over the last decade, test smells were heavily studied to…
What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning
Patrick Keller, Laura Plein, Tegawendé F. Bissyandé +2
Recent successes in training word embeddings for NLP tasks have encouraged a wave of research on representation learning for source code, which builds on similar NLP methods. The o…
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé +4
Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurr…