220 citations · 1k across the 53 of their papers we have counts for
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Syntactic Vs. Semantic similarity of Artificial and Real Faults in Mutation Testing Studies
Milos Ojdanic, Aayush Garg, Ahmed Khanfir +3
Fault seeding is typically used in controlled studies to evaluate and compare test techniques. Central to these techniques lies the hypothesis that artificially seeded faults invol…
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models
Yuejun Guo, Qiang Hu, Maxime Cordy +2
Active learning is an established technique to reduce the labeling cost to build high-quality machine learning models. A core component of active learning is the acquisition functi…
Cerebro: Static Subsuming Mutant Selection
Aayush Garg, Milos Ojdanic, Renzo Degiovanni +3
Mutation testing research has indicated that a major part of its application cost is due to the large number of low utility mutants that it introduces. Although previous research h…
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space
Thibault Simonetto, Salijona Dyrmishi, Salah Ghamizi +2
The generation of feasible adversarial examples is necessary for properly assessing models that work in constrained feature space. However, it remains a challenging task to enforce…
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,…