Exploring the Fundamentals of Mutations in Deep Neural Networks

The increasing popularity of deep neural networks (DNNs) has led to the adaptation of mutation analysis from classical software development to the machine learning (ML) paradigm. However, determining what to mutate and to what extent remains a challenge. To this aim, two questions are of importance: (i) which ML artifacts can be modified to generate acceptable mutations?, and (ii) what extent of change in a specific metric qualifies as a useful mutant?