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Generating Test Data Using Symbolic Execution: Challenges with Floating Point Data Types

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Information and Software Technologies (ICIST 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 319))

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Abstract

Dynamic compositional symbolic execution is a well-known white-box method for generating unit test data. However there exists certain issues when applying this method for software that heavily depends on floating point data types. In this paper we present these issues and suggest our solution. Our presented method would integrate two techniques: symbolic execution and search-based testing to increase code coverage of software under test. We have implemented it as an extension to tool PEX, that is being developed at Microsoft Research. Our extension implements search-based testing as an optimization technique using AVM method. We present coverage comparison for several benchmark functions.

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Author information

Authors and Affiliations

  1. Software Engineering Department, Kaunas University of Technology, Studentu str. 50-406, Kaunas, Lithuania

    Justinas Prelgauskas & Eduardas Bareisa

Authors
  1. Justinas Prelgauskas
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  2. Eduardas Bareisa
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Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Studentu g. 50-313a, LT-51368, Kaunas, Lithuania

    Tomas Skersys  & Rimantas Butleris  & 

  2. Kaunas University of Technology, Studentu g. 50-309a, LT-51368, Kaunas, Lithuania

    Rita Butkiene

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© 2012 Springer-Verlag Berlin Heidelberg

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Prelgauskas, J., Bareisa, E. (2012). Generating Test Data Using Symbolic Execution: Challenges with Floating Point Data Types. In: Skersys, T., Butleris, R., Butkiene, R. (eds) Information and Software Technologies. ICIST 2012. Communications in Computer and Information Science, vol 319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33308-8_22

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  • DOI: https://doi.org/10.1007/978-3-642-33308-8_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33307-1

  • Online ISBN: 978-3-642-33308-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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