Classifying Mutants with Decomposition Kernel

  • Joanna Strug
  • Barbara StrugEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9692)


The paper deals with the problem of reducing the cost of mutation testing using artificial intelligence methods. The presented approach is based on the similarity of mutants. The mutants are coded as control flow diagrams representing the programs structure, variables and conditions. The similarity is then calculated with the use of a new graph kernel and used to predict if a given test case detects a mutant or not. The prediction process is performed by a classification algorithm. Experimental results are also presented in this paper on the basis of two systems.


Mutation testing Machine learning Graph distance Classification Test evaluation 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Electrical and Computer EngineeringCracow University of TechnologyKrakowPoland
  2. 2.Department of Physics, Astronomy and Applied Computer ScienceJagiellonian UniversityKrakowPoland

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