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Found 6 papers, 6 papers with code
Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing
Our empirical evaluation shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin.
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study
In this paper, we investigate to what extent TIGs can generate valid inputs, according to both automated and human validators.
DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
The adequacy of the test data used to test such systems can be assessed by their ability to expose artificially injected faults (mutations) that simulate real DL faults.
DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search
Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones.
Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing
If the frontier of misbehaviours is outside the validity domain of the system, the quality check is passed.
Taxonomy of Real Faults in Deep Learning Systems
The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance.
