%0 Conference Proceedings
%T Machine Learning Approach in Mutation Testing
%+ Cracow University of Technology
%+ Uniwersytet Jagielloński w Krakowie = Jagiellonian University (UJ)
%A Strug, Joanna
%A Strug, Barbara
%Z Part 6: New Testing Methods
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 24th International Conference on Testing Software and Systems (ICTSS)
%C Aalborg, Denmark
%Y Brian Nielsen
%Y Carsten Weise
%I Springer
%3 Testing Software and Systems
%V LNCS-7641
%P 200-214
%8 2012-11-19
%D 2012
%R 10.1007/978-3-642-34691-0_15
%K mutation testing
%K machine learning
%K graph distance
%K classification
%K test evaluation
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X 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.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-01482402/document
%2 https://inria.hal.science/hal-01482402/file/978-3-642-34691-0_15_Chapter.pdf
%L hal-01482402
%U https://inria.hal.science/hal-01482402
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-ICTSS
%~ IFIP-LNCS-7641