# Using Semi-Supervised Learning for Predicting Metamorphic Relations

arXiv: https://arxiv.org/abs/1802.07324 · HTML: https://mlsanity.com/paper/1802.07324/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study](https://mlsanity.com/paper/2206.10210/) (2022)

## Abstract (arXiv)

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.