Using Semi-Supervised Learning for Predicting Metamorphic Relations

Bonnie Hardin; Upulee Kanewala

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arXiv · 1802.07324

Using Semi-Supervised Learning for Predicting Metamorphic Relations

Bonnie Hardin·Upulee Kanewala

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Abstract

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.

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Bonnie Hardin, Upulee Kanewala. 2018-02-20. Using Semi-Supervised Learning for Predicting Metamorphic Relations. https://arxiv.org/abs/1802.07324

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