Coverage Testing of Deep Learning Models using Dataset Characterization

Understand

Deep Neural Networks (DNNs), with its promising performance, are being increasingly used in safety critical applications such as autonomous driving, cancer detection, and secure authentication.

• With growing importance in deep learning, there is a requirement for a more standardized framework to evaluate and test deep learning models.
• The primary challenge involved in automated generation of extensive test cases are: (i) neural networks are difficult to interpret and debug and (ii) availability of human annotators to generate specialized test points.
• In this research, we explain the necessity to measure the quality of a dataset and propose a test case generation system guided by the dataset properties.