187 citations · 278 across the 29 of their papers we have counts for
7 papers · 1 filter
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…