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International Symposium on Search Based Software Engineering

SSBSE 2016: Search Based Software Engineering pp 267–273Cite as

A Method Dependence Relations Guided Genetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9962))

Abstract

Search based test generation approaches have already been shown to be effective for generating test data that achieves high code coverage for object-oriented programs. In this paper, we present a new search-based approach, called GAMDR, that uses a genetic algorithm (GA) to generate test data. GAMDR exploits method dependence relations (MDR) to narrow down the search space and direct mutation operators to the most beneficial regions for achieving high branch coverage. We compared GAMDR’s effectiveness with random testing, EvoSuite, and a simple GA. The tests generated by GAMDR achieved higher branch coverage.

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Notes

  1. 1.

    http://www.sable.mcgill.ca/.

  2. 2.

    http://eclemma.org/jacoco/.

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Author information

Authors and Affiliations

  1. Oregon State University, Corvallis, OR, 97330, USA

    Ali Aburas & Alex Groce

Authors
  1. Ali Aburas
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  2. Alex Groce
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Corresponding author

Correspondence to Ali Aburas .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    Federica Sarro

  2. Michigan State University , East Lansing, Michigan, USA

    Kalyanmoy Deb

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© 2016 Springer International Publishing AG

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Cite this paper

Aburas, A., Groce, A. (2016). A Method Dependence Relations Guided Genetic Algorithm. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-47106-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47105-1

  • Online ISBN: 978-3-319-47106-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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