  Software testing is difficult to automate, especially in programs which have
no oracle, or method of determining which output is correct. Metamorphic
testing is a solution this problem. Metamorphic testing uses metamorphic
relations to define test cases and expected outputs. A large amount of time is
needed for a domain expert to determine which metamorphic relations can be used
to test a given program. Metamorphic relation prediction removes this need for
such an expert. We propose a method using semi-supervised machine learning to
detect which metamorphic relations are applicable to a given code base. We
compare this semi-supervised model with a supervised model, and show that the
addition of unlabeled data improves the classification accuracy of the MR
prediction model.
