5 papers
Effort-Optimized, Accuracy-Driven Labelling and Validation of Test Inputs for DL Systems: A Mixed-Integer Linear Programming Approach
Mohammad Hossein Amini, Mehrdad Sabetzadeh, Shiva Nejati
Software systems increasingly include AI components based on deep learning (DL). Reliable testing of such systems requires near-perfect test-input validity and label accuracy, with…
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…