Using classification for cost reduction of applying mutation testing

The paper uses machine learning methods to deal with the problem of reducing the cost of applying mutation testing. A method of classifying mutants of a program using structural similarity is proposed. To calculate such a similarity each mutant is firstly converted into a hierarchical graph, which represents the mutant's control flow, variables and conditions. Then using such a graph form graph kernels are introduced to calculate similarity among mutants. The classification algorithm is then applied for prediction. This approach helps to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented.