133 citations · 365 across the 73 of their papers we have counts for
6 papers · 1 filter
IBIR: Bug Report driven Fault Injection
Ahmed Khanfir, Anil Koyuncu, Mike Papadakis +4
Much research on software engineering and software testing relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-…
Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data
Aayush Garg, Renzo Degiovanni, Matthieu Jimenez +3
Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classif…
Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers
Adriano Franci, Maxime Cordy, Martin Gubri +2
Graph-based Semi-Supervised Learning (GSSL) is a practical solution to learn from a limited amount of labelled data together with a vast amount of unlabelled data. However, due to…
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
Martin Gubri, Maxime Cordy, Mike Papadakis +2
An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on an ensemble-based surrogate to increase diversity. We argue t…
Data-driven Simulation and Optimization for Covid-19 Exit Strategies
Salah Ghamizi, Renaud Rwemalika, Lisa Veiber +5
The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those meas…
Killing Stubborn Mutants with Symbolic Execution
Thierry Titcheu Chekam, Mike Papadakis, Maxime Cordy +1
We introduce SeMu, a Dynamic Symbolic Execution technique that generates test inputs capable of killing stubborn mutants (killable mutants that remain undetected after a reasonable…