Machine Learning Approach in Mutation Testing

Abstract : This paper deals with an approach based on the similarity of mutants. This similarity is used to reduce the number of mutants to be executed. In order to calculate such a similarity among mutants their structure is used. Each mutant is converted into a hierarchical graph, which represents the program’s flow, variables and conditions. On the basis of this graph form a special graph kernel is defined to calculate similarity among programs. It is then used to predict whether a given test would detect a mutant or not. The prediction is carried out with the help of a classification algorithm. This approach should help to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented in this paper. An example of a program used in experiments is described and the results obtained, especially classification errors, are presented.
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Brian Nielsen; Carsten Weise. 24th International Conference on Testing Software and Systems (ICTSS), Nov 2012, Aalborg, Denmark. Springer, Lecture Notes in Computer Science, LNCS-7641, pp.200-214, 2012, Testing Software and Systems. 〈10.1007/978-3-642-34691-0_15〉
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Joanna Strug, Barbara Strug. Machine Learning Approach in Mutation Testing. Brian Nielsen; Carsten Weise. 24th International Conference on Testing Software and Systems (ICTSS), Nov 2012, Aalborg, Denmark. Springer, Lecture Notes in Computer Science, LNCS-7641, pp.200-214, 2012, Testing Software and Systems. 〈10.1007/978-3-642-34691-0_15〉. 〈hal-01482402〉

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