  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. From a testing perspective, four different dataset
quality dimensions are proposed: (i) equivalence partitioning, (ii) centroid
positioning, (iii) boundary conditioning, and (iv) pair-wise boundary
conditioning. The proposed system is evaluated on well known image
classification datasets such as MNIST, Fashion-MNIST, CIFAR10, CIFAR100, and
SVHN against popular deep learning models such as LeNet, ResNet-20, VGG-19.
Further, we conduct various experiments to demonstrate the effectiveness of
systematic test case generation system for evaluating deep learning models.
