  Testing has been widely recognised as difficult for AI applications. This
paper proposes a set of testing strategies for testing machine learning
applications in the framework of the datamorphism testing methodology. In these
strategies, testing aims at exploring the data space of a classification or
clustering application to discover the boundaries between classes that the
machine learning application defines. This enables the tester to understand
precisely the behaviour and function of the software under test. In the paper,
three variants of exploratory strategies are presented with the algorithms
implemented in the automated datamorphic testing tool Morphy. The correctness
of these algorithms are formally proved. Their capability and cost of
discovering borders between classes are evaluated via a set of controlled
experiments with manually designed subjects and a set of case studies with real
machine learning models.
