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Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing

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Book cover Genetic Programming (EuroGP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12101))

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Abstract

Software-based optimization techniques have been increasingly used to automate code coverage analysis since the nineties. Although several studies suggest that interdependencies can exist between condition constructs in branching conditions of real life programs e.g. (\(i<=100\)) or (\(i==j\)), etc., to date, only the Ariadne system, a Grammatical Evolution (GE)-based Search Based Software Testing (SBST) technique, exploits interdependencies between variables to efficiently automate code coverage analysis.

Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to very efficiently evolve highly complex test cases, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from the interdependencies involving constants e.g. (\(i<=100\)), which are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision.

We propose to seed the grammar with constants extracted from the source code of the program under test in order to enhance and extend Ariadne’s capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude.

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Notes

  1. 1.

    In order to facilitate future comparisons we have made available the source code at http://bds.ul.ie/?page_id=390/.

  2. 2.

    The source code of these benchmark functions was made available by [11] at http://bds.ul.ie/?page_id=390/.

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Acknowledgments

The authors would like to thank Aidan Murphy, Muhammad Hamad Khan and Sehrish Saeed for their help with conceptualization of the idea, graphic designs and benchmark functions, respectively. This work is supported by the Science Foundation of Ireland (SFI) Grant Number 16/IA/4605.

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Authors and Affiliations

  1. Biocomputing and Developmental Systems Group, Department of Computer Science and Information Systems, Lero - The Irish Software Research Centre, University of Limerick, Limerick, Ireland

    Muhammad Sheraz Anjum & Conor Ryan

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  1. Muhammad Sheraz Anjum
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  2. Conor Ryan
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Correspondence to Muhammad Sheraz Anjum .

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Editors and Affiliations

  1. Queen's University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

  4. Pablo de Olavide University, Seville, Spain

    Federico Divina

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Anjum, M.S., Ryan, C. (2020). Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-44094-7_2

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