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Creators/Authors contains: "Rahman, Karishma"

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  1. ; ; ( , Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops)
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  2. ; ( , the 3rd International Workshop on Metamorphic Testing)
    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. 
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