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