Skip to Main content Skip to Navigation
New interface
Conference papers

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.
Complete list of metadata

Cited literature [47 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, March 3, 2017 - 3:17:20 PM
Last modification on : Tuesday, December 7, 2021 - 3:33:15 PM
Long-term archiving on: : Tuesday, June 6, 2017 - 12:33:25 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Joanna Strug, Barbara Strug. Machine Learning Approach in Mutation Testing. 24th International Conference on Testing Software and Systems (ICTSS), Nov 2012, Aalborg, Denmark. pp.200-214, ⟨10.1007/978-3-642-34691-0_15⟩. ⟨hal-01482402⟩



Record views


Files downloads