Using Structural Similarity to Classify Tests in Mutation Testing

Mutation testing is an effective technique for assessing quality of tests provided for a system. However it suffers from high computational cost of executing mutants of the system. In this paper a method of classifying such mutants is proposed. This classification is based on using an edit distance kernel and k-NN classifier. Using the results of this classification it is possible to predict whether a mutant would be detected by tests or not. Thus the application of the approach can help to lower the number of mutants that have to be executed and so also to lower the cost of using the mutation testing.

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