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Field Report: Applying Monte Carlo Tree Search for Program Synthesis

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Search Based Software Engineering (SSBSE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9962))

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Abstract

Program synthesis aims to automatically generate an executable segment of code that satisfies a given set of criteria. Genetic programming has been widely studied for program synthesis. However, it has drawbacks such as code bloats and the difficulty in finer control over the growth of programs. This paper explores the possibility of applying Monte Carlo Tree Search (MCTS) technique to general purpose program synthesis. The exploratory study applies MCTS to synthesis of six small benchmarks using Java Bytecode instructions, and compares the results to those of genetic programming. The paper discusses the major challenges and outlines the future work.

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Notes

  1. 1.

    A brief overview of MCTS, as well as details of the experimental results, is available from http://coinse.kaist.ac.kr/projects/mctsps.

  2. 2.

    http://commons.apache.org/bcel/.

  3. 3.

    http://pyevolve.sourceforge.net.

  4. 4.

    GP should generate programs of lengths similar to \(l_p\). As most non-terminals have one or two leaves, maximum depth of 7 achieves this.

  5. 5.

    Detailed statistics, as well as the output program instructions, are available from http://coinse.kaist.ac.kr/projects/mctsps.

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Acknowledgments

Authors would like to thank David White and Kee-eung Kim for many thoughtful discussions about Monte Carlo Tree Search. This research has been supported by Undergraduate Research Program (URP) at KAIST.

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Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology, 291 Daehak Ro, Yuseong Gu, Daejeon, Republic of Korea

    Jinsuk Lim & Shin Yoo

Authors
  1. Jinsuk Lim
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  2. Shin Yoo
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Correspondence to Jinsuk Lim .

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Editors and Affiliations

  1. University College London, London, United Kingdom

    Federica Sarro

  2. Michigan State University , East Lansing, Michigan, USA

    Kalyanmoy Deb

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Lim, J., Yoo, S. (2016). Field Report: Applying Monte Carlo Tree Search for Program Synthesis. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_27

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  • DOI: https://doi.org/10.1007/978-3-319-47106-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47105-1

  • Online ISBN: 978-3-319-47106-8

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