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Dynamic Symbolic Execution Guided by Data Dependency Analysis for High Structural Coverage

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Book cover Evaluation of Novel Approaches to Software Engineering (ENASE 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 410))

Abstract

Dynamic symbolic execution has been shown to be an effective technique for automated test input generation. When applied to large-scale and complex programs, its scalability however is limited due to the combinatorial explosion of the path space. We propose to take advantage of data flow analysis to better perform dynamic symbolic execution in the context of generating test inputs for maximum structural coverage. In particular, we utilize the chaining mechanism to (1) extract precise guidance to direct dynamic symbolic execution towards exploring uncovered code elements and (2) meanwhile significantly optimize the path exploration process. Preliminary experiments conducted to evaluate the performance of the proposed approach have shown very encouraging results.

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Author information

Authors and Affiliations

  1. School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

    TheAnh Do, A. C. M. Fong & Russel Pears

Authors
  1. TheAnh Do
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  2. A. C. M. Fong
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  3. Russel Pears
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Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, 2109, Sydney, NSW, Australia

    Leszek A. Maciaszek

  2. INSTICC and IPS, Estefanilha, Setúbal, Portugal

    Joaquim Filipe

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Do, T., Fong, A.C.M., Pears, R. (2013). Dynamic Symbolic Execution Guided by Data Dependency Analysis for High Structural Coverage. In: Maciaszek, L.A., Filipe, J. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2012. Communications in Computer and Information Science, vol 410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45422-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-45422-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45421-9

  • Online ISBN: 978-3-642-45422-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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