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Title: Predicting metamorphic relations for matrix calculation programs
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.
Authors:
;
Award ID(s):
1656877
Publication Date:
NSF-PAR ID:
10062905
Journal Name:
the 3rd International Workshop on Metamorphic Testing
Page Range or eLocation-ID:
10 to 13
Sponsoring Org:
National Science Foundation
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