  Metamorphic testing seeks to verify software in the absence of test oracles.
Our application domain is ocean system modeling, where test oracles rarely
exist, but where symmetries of the simulated physical systems are known. The
input data set is large owing to the requirements of the application domain.
This paper presents work in progress for the automated generation of
metamorphic test scenarios using machine learning. We extended our previously
proposed method [1] to identify metamorphic relations with reduced
computational complexity. Initially, we represent metamorphic relations as
identity maps. We construct a cost function that minimizes for identifying a
metamorphic relation orthogonal to previously found metamorphic relations and
penalize for the identity map. A machine learning algorithm is used to identify
all possible metamorphic relations minimizing the defined cost function. We
propose applying dimensionality reduction techniques to identify attributes in
the input which have high variance among the identified metamorphic relations.
We apply mutation on these selected attributes to identify distinct metamorphic
relations with reduced computational complexity. For experimental evaluation,
we subject the two implementations of an ocean-modeling application to the
proposed method to present the use of metamorphic relations to test the two
implementations of this application.
