Distribution Awareness for AI System Testing
This addresses the need for reliable testing in safety-critical AI applications, though it is incremental as it builds on existing testing methods by incorporating distribution awareness.
The paper tackles the problem of generating meaningful test cases for deep learning systems by proposing an out-of-distribution-guided testing technique, which filters up to 55.44% of error test cases on CIFAR-10 and improves robustness by 10.05%.
As Deep Learning (DL) is continuously adopted in many safety critical applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. Although recent progress has been made in designing novel testing techniques for DL software, the distribution of generated test data is not taken into consideration. It is therefore hard to judge whether the identified errors are indeed meaningful errors to the DL application. Therefore, we propose a new OOD-guided testing technique which aims to generate new unseen test cases relevant to the underlying DL system task. Our results show that this technique is able to filter up to 55.44% of error test case on CIFAR-10 and is 10.05% more effective in enhancing robustness.