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Automated Generation of Unit Tests for Refactoring

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Extreme Programming and Agile Processes in Software Engineering (XP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3092))

Abstract

The key issue for effective refactoring is to ensure that observable behavior of the code will not change. Use of regression tests is the suggested method for verifying this property, but they appear to be not well suited for applying refactorings. The tests verify the domain-specific code relations, and often neglect properties important for the given refactoring or check the ones that actually change. In the paper we present a concept of generic refactoring tests, which are oriented toward the refactorings rather than the code. They can be generated automatically, are able to adapt to changes introduced by refactorings and learn the specifics of the tested code.

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References

  1. Beck, K.: Extreme Programming Explained. Embrace Change. Addison-Wesley, Reading (2000)

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  5. Walter, B.: Extending Testability for Automated Refactoring. In: Marchesi, M., Succi, G. (eds.) XP 2003. LNCS, vol. 2675, pp. 429–430. Springer, Heidelberg (2003)

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Poland

    Bartosz Walter & Blazej Pietrzak

Authors
  1. Bartosz Walter
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  2. Blazej Pietrzak
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Editor information

Editors and Affiliations

  1. No Affiliation,  

    Jutta Eckstein

  2. Informatik og Matematisk Modellering, Danmarks Tekniske Universitet, Lyngby,  

    Hubert Baumeister

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© 2004 Springer-Verlag Berlin Heidelberg

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Walter, B., Pietrzak, B. (2004). Automated Generation of Unit Tests for Refactoring. In: Eckstein, J., Baumeister, H. (eds) Extreme Programming and Agile Processes in Software Engineering. XP 2004. Lecture Notes in Computer Science, vol 3092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24853-8_25

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  • DOI: https://doi.org/10.1007/978-3-540-24853-8_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22137-1

  • Online ISBN: 978-3-540-24853-8

  • eBook Packages: Springer Book Archive

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