2 citations · 2 across the 5 of their papers we have counts for
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Test Input Validation for Vision-based DL Systems: An Active Learning Approach
Delaram Ghobari, Mohammad Hossein Amini, Dai Quoc Tran +3
Testing deep learning (DL) systems requires extensive and diverse, yet valid, test inputs. While synthetic test input generation methods, such as metamorphic testing, are widely us…
Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
Lev Sorokin, Damir Safin, Shiva Nejati
Search-based software testing (SBST) is a widely adopted technique for testing complex systems with large input spaces, such as Deep Learning-enabled (DL-enabled) systems. Many SBS…
Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems
Mohammad Hossein Amini, Shiva Nejati
Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic simulator images. This approach results in tr…
Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach
Cristopher McIntyre-Garcia, Adrien Heymans, Beril Borali +2
Deep Learning (DL) models excel in computer vision tasks but can be susceptible to adversarial examples. This paper introduces Triple-Metric EvoAttack (TM-EVO), an efficient algori…
Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems
Mohammad Hossein Amini, Shervin Naseri, Shiva Nejati
Simulators are widely used to test Autonomous Driving Systems (ADS), but their potential flakiness can lead to inconsistent test results. We investigate test flakiness in simulatio…