Automated identification of metamorphic test scenarios for an ocean-modeling application
Dilip J. Hiremath, Martin Claus, Wilhelm Hasselbring, Willi Rath

TL;DR
This paper proposes a machine learning-based method to automatically generate metamorphic test scenarios for ocean modeling software, addressing the challenge of lacking test oracles in scientific simulations.
Contribution
It introduces a novel approach to automatically identify metamorphic relations using machine learning, enhancing testing capabilities for complex scientific applications.
Findings
Successfully identified multiple metamorphic relations in ocean models
Demonstrated the method's potential to improve regression testing
Applicable to other scientific software lacking test oracles
Abstract
Metamorphic testing seeks to validate software in the absence of test oracles. Our application domain is ocean modeling, where test oracles often do not exist, but where symmetries of the simulated physical systems are known. In this short paper we present work in progress for automated generation of metamorphic test scenarios using machine learning. Metamorphic testing may be expressed as f(g(X))=h(f(X)) with f being the application under test, with input data X, and with the metamorphic relation (g, h). Automatically generated metamorphic relations can be used for constructing regression tests, and for comparing different versions of the same software application. Here, we restrict to h being the identity map. Then, the task of constructing tests means finding different g which we tackle using machine learning algorithms. These algorithms typically minimize a cost function. As one…
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