  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.
