Chairperson, Graduate Committee: Upulee KanewalaHardin, Bonnie Elizabeth2018-09-202018-09-202018https://scholarworks.montana.edu/handle/1/14551Software testing is difficult to automate, especially in programs which face the oracle problem, where an oracle does not exist, or is too hard to develop. Metamorphic testing is a solution to this problem. Metamorphic testing uses metamorphic relations to determine if tests pass or fail. 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 learning algorithms to detect which metamorphic relations are applicable to a given code base. Semi-supervised learning is useful in this problem domain as most programs do not have pre-defined metamorphic relations. These programs are considered unlabeled data in a semi-supervised algorithm. We compare two semi-supervised models with a supervised model, and show that the addition of unlabeled data improves the classification accuracy of the metamorphic relation prediction model.enComputer softwareTestingMachine learningAlgorithmsThesisCopyright 2018 by Bonnie Elizabeth Hardin