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On Comprehension of Genetic Programming Solutions: A Controlled Experiment on Semantic Inference

Author

Listed:
  • Boštjan Slivnik

    (Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000 Ljubljana, Slovenia)

  • Željko Kovačević

    (Department of Computer Science and Informatics, Zagreb University of Applied Sciences, Vrbik 8, 10000 Zagreb, Croatia)

  • Marjan Mernik

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia)

  • Tomaž Kosar

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia)

Abstract

Applied to the problem of automatic program generation, Genetic Programming often produces code bloat, or unexpected solutions that are, according to common belief, difficult to comprehend. To study the comprehensibility of the code produced by Genetic Programming, attribute grammars obtained by Genetic Programming-based semantic inference were compared to manually written ones. According to the established procedure, the research was carried out as a controlled classroom experiment that involved two groups of students from two universities, and consisted of a background questionnaire, two tests and a feedback questionnaire after each test. The tasks included in the tests required the identification of various properties of attributes and grammars, the identification of the correct attribute grammar from a list of choices, or correcting a semantic rule in an attribute grammar. It was established that solutions automatically generated by Genetic Programming in the field of semantic inference, in this study attribute grammars, are indeed significantly harder to comprehend than manually written ones. This finding holds, regardless of whether comprehension correctness, i.e., how many attribute grammars were correctly comprehended, or comprehension efficiency is considered, i.e., how quickly attribute grammars were correctly comprehended.

Suggested Citation

  • Boštjan Slivnik & Željko Kovačević & Marjan Mernik & Tomaž Kosar, 2022. "On Comprehension of Genetic Programming Solutions: A Controlled Experiment on Semantic Inference," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3386-:d:917928
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    References listed on IDEAS

    as
    1. Matej Črepinšek & Shih-Hsi Liu & Marjan Mernik & Miha Ravber, 2019. "Long Term Memory Assistance for Evolutionary Algorithms," Mathematics, MDPI, vol. 7(11), pages 1-25, November.
    2. Željko Kovačević & Marjan Mernik & Miha Ravber & Matej Črepinšek, 2020. "From Grammar Inference to Semantic Inference—An Evolutionary Approach," Mathematics, MDPI, vol. 8(5), pages 1-24, May.
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    Cited by:

    1. Tomaž Kosar & Željko Kovačević & Marjan Mernik & Boštjan Slivnik, 2023. "The Impact of Code Bloat on Genetic Program Comprehension: Replication of a Controlled Experiment on Semantic Inference," Mathematics, MDPI, vol. 11(17), pages 1-20, August.

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    1. Tomaž Kosar & Željko Kovačević & Marjan Mernik & Boštjan Slivnik, 2023. "The Impact of Code Bloat on Genetic Program Comprehension: Replication of a Controlled Experiment on Semantic Inference," Mathematics, MDPI, vol. 11(17), pages 1-20, August.

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