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Found 6 papers, 2 papers with code
Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach
Deep Learning (DL) models excel in computer vision tasks but can be susceptible to adversarial examples.
Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems
Our empirical results, obtained from two widely-used open-source ADS simulators and five diverse ADS test setups, show that test flakiness in ADS is a common occurrence and can significantly impact the test results obtained by randomized algorithms.
Optimal Priority Assignment for Real-Time Systems: A Coevolution-Based Approach
This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines.
Software Engineering
Can Offline Testing of Deep Neural Networks Replace Their Online Testing?
Further, we cannot exploit offline testing results to reduce the cost of online testing in practice since we are not able to identify specific situations where offline testing could be as accurate as online testing in identifying safety requirement violations.
Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators
Based on a minimalistic scene, we compare critical test scenarios generated using our SBST solution in these two simulators.
Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study
Further, offline testing is more optimistic than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing.
