8 citations · 23 across the 10 of their papers we have counts for
12 papers
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
Qiang Hu, Yuejun Guo, Maxime Cordy +4
Deep Neural Networks (DNNs) have gained considerable attention in the past decades due to their astounding performance in different applications, such as natural language modeling,…
GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses
Wei Ma, Mengjie Zhao, Ezekiel Soremekun +6
Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs t…
Discerning Legitimate Failures From False Alerts: A Study of Chromium's Continuous Integration
Guillaume Haben, Sarra Habchi, Mike Papadakis +2
Flakiness is a major concern in Software testing. Flaky tests pass and fail for the same version of a program and mislead developers who spend time and resources investigating test…
Adversarial Robustness in Multi-Task Learning: Promises and Illusions
Salah Ghamizi, Maxime Cordy, Mike Papadakis +1
Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets,…
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles
Yuejun Guo, Qiang Hu, Maxime Cordy +2
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which causes serious threats to security-critical applications. This motivated much research on providing mechan…
On the Use of Mutation in Injecting Test Order-Dependency
Sarra Habchi, Maxime Cordy, Mike Papadakis +1
Background: Test flakiness is identified as a major issue that compromises the regression testing process of complex software systems. Flaky tests manifest non-deterministic behavi…