# Metamorphic Relation Prioritization for Effective Regression Testing

arXiv: https://arxiv.org/abs/2109.09798 · HTML: https://mlsanity.com/paper/2109.09798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Empirical Evaluation of Mutation-based Test Prioritization Techniques](https://mlsanity.com/paper/1709.04631/) (2017)
- [Quality Assurance of Bioinformatics Software: A Case Study of Testing a Biomedical Text Processing Tool Using Metamorphic Testing](https://mlsanity.com/paper/1802.07354/) (2018)
- [Testing Scientific Software: A Systematic Literature Review](https://mlsanity.com/paper/1804.01954/) (2018)

## Built on by (most cited first, 1 of 1)

- [Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detection](https://mlsanity.com/paper/2505.07870/) (2025)

## Abstract (arXiv)

Metamorphic testing (MT) is widely used for testing programs that face the oracle problem. It uses a set of metamorphic relations (MRs), which are relations among multiple inputs and their corresponding outputs to determine whether the program under test is faulty. Typically, MRs vary in their ability to detect faults in the program under test, and some MRs tend to detect the same set of faults. In this paper, we propose approaches to prioritize MRs to improve the efficiency and effectiveness of MT for regression testing. We present two MR prioritization approaches: (1) fault-based and (2) coverage-based. To evaluate these MR prioritization approaches, we conduct experiments on three complex open-source software systems. Our results show that the MR prioritization approaches developed by us significantly outperform the current practice of executing the source and follow-up test cases of the MRs in an ad-hoc manner in terms of fault detection effectiveness. Further, fault-based MR prioritization leads to reducing the number of source and follow-up test cases that needs to be executed as well as reducing the average time taken to detect a fault, which would result in saving time and cost during the testing process.