DESIGNATOR: a Toolset for Automated GAN-enhanced Search-based Testing and Retraining of DNNs in Martian Environments

We present DESIGNATOR, a toolset for generating datasets for testing and retraining deep neural networks (DNNs) performing computer vision tasks in Martian-like environments. The toolset integrates Marsim, a simulator of the Mars environment, and DESIGNATE, a search-based approach combining simulation, generative adversarial networks (GANs), and search-based test input generation. The tool enables users to select a search strategy, launch simulations in MarsSim, and observe the evolution of simulated images, corresponding realistic images, ground truth labels, model predictions, and fitness values. Beyond supporting researchers and practitioners in generating datasets capable of identifying failures and retraining DNNs, DESIGNATOR can be used as a didactic tool to explain how image datasets can be generated using meta-heuristic search. Furthermore, MarsSim can be used standalone, through its API and GUI. MarsSim enables researchers to assess search-based approaches beyond the predominantly studied automotive context. A demo video of DESIGNATOR is available at https: //youtu.be/fGxgpgViPEo.