The approach uses neuron coverage to activate as many neurons as possible during the search process, with the assumption that observing the decision of the model when all the neurons are activated increases confidence in the trustworthiness of the model. The search process uses local and global coverage to maximize the coverage and consider what was covered in previous iterations. The criteria used for the search process include maximizing the occurrences of relevant test cases and preserving the semantic of the input data.