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European Conference on Genetic Programming (Part of EvoStar)

EuroGP 2022: Genetic Programming pp 262–277Cite as

Exploiting Knowledge from Code to Guide Program Search

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13223))

Abstract

Human code is different from code generated by program search. We investigate if properties from human-generated code can guide program search to improve the qualities of the generated programs, e.g., readability and performance. Here we focus on program search with grammatical evolution, which produces code that has different structure compared to human-generated code, e.g., loops and conditions are hardly used. We use a large code-corpus that was mined from the open software repository service GitHub and measure software metrics and properties describing the code-base. We use this knowledge to guide the search by incorporating a new selection scheme. Our new selection scheme favors programs that are structurally similar to the programs in the GitHub code-base. We find noticeable evidence that software metrics can help in guiding evolutionary search.

The authors thank Jordan Wick for sharing his expertise, the insightful discussions, and his help on our project. This work was supported by a fellowship within the IFI programme of the German Academic Exchange Service (DAAD).

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Notes

  1. 1.

    radon: https://pypi.org/project/radon/.

  2. 2.

    astdump: https://pypi.org/project/astdump/.

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Author information

Authors and Affiliations

  1. Baden-Wuerttemberg Cooperative State University, Heidenheim, Germany

    Dirk Schweim

  2. MIT CSAIL, Cambridge, MA, USA

    Erik Hemberg & Una-May O’Reilly

  3. Johannes Gutenberg University, Mainz, Germany

    Dominik Sobania

Authors
  1. Dirk Schweim
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  2. Erik Hemberg
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  3. Dominik Sobania
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  4. Una-May O’Reilly
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Corresponding author

Correspondence to Dirk Schweim .

Editor information

Editors and Affiliations

  1. University of Trieste, Trieste, Italy

    Eric Medvet

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  3. Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

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Schweim, D., Hemberg, E., Sobania, D., O’Reilly, UM. (2022). Exploiting Knowledge from Code to Guide Program Search. In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. Springer, Cham. https://doi.org/10.1007/978-3-031-02056-8_17

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  • DOI: https://doi.org/10.1007/978-3-031-02056-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02055-1

  • Online ISBN: 978-3-031-02056-8

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