1 citations · 1 across the 2 of their papers we have counts for
3 papers
Feature-Aware Test Generation for Deep Learning Models
Xingcheng Chen, Oliver Weissl, Andrea Stocco
As deep learning models are widely used in software systems, test generation plays a crucial role in assessing the quality of such models before deployment. To date, the most advan…
XMutant: XAI-based Fuzzing for Deep Learning Systems
Xingcheng Chen, Matteo Biagiola, Vincenzo Riccio +2
Semantic-based test generators are widely used to produce failure-inducing inputs for Deep Learning (DL) systems. They typically generate challenging test inputs by applying random…
Benchmarking Generative AI Models for Deep Learning Test Input Generation
Maryam, Matteo Biagiola, Andrea Stocco +1
Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets…