Using semi-supervised learning for predicting metamorphic relations

Abstract

Software 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.