  Matrices often represent important information in scientific applications and
are involved in performing complex calculations. But systematically testing
these applications is hard due to the oracle problem. Metamorphic testing is an
effective approach to test such applications because it uses metamorphic
relations to determine whether test cases have passed or failed. Metamorphic
relations are typically identified with the help of a domain expert and is a
labor intensive task. In this work we use a graph kernel based machine learning
approach to predict metamorphic relations for matrix calculation programs.
Previously, this graph kernel based machine learning approach was used to
successfully predict metamorphic relations for programs that perform numerical
calculations. Results of this study show that this approach can be used to
predict metamorphic relations for matrix calculation programs as well.
