Software Engineering for Dataset Augmentation using Generative Adversarial Networks

Software engineers require a large amount of data for building neural network-based software systems. The engineering of these data is often neglected, though, it is a critical and time-consuming activity. In this work, we present a novel software engineering approach for dataset augmentation using neural networks. We propose a rigorous process for generating synthetic data to improve the training of neural networks. Also, we demonstrate our approach to successfully improve the recognition of handwritten digits using conditional generative
adversarial networks (cGAN). Finally, we shortly discuss selected important issues of our process, presenting related work and proposing some improvements.