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Cost Reduction in Mutation Testing with Bytecode-Level Mutants Classification

  • Joanna Strug
  • Barbara StrugEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

The paper presents the application of classification based approach to software quality domain. In particular it deals with the issue of reducing the cost of mutation testing. The presented approach is based on the similarity of mutants represented at the bytecode level. The distance matrix for mutants is used in kNN algorithm to predict if a given test set detects a mutant or not. Experimental results are also presented in this paper on the basis of two systems. The obtained results show the usefulness of the proposed method.

Keywords

Machine learning Mutation testing Bytecode distance Classification Test evaluation 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

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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