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The Impact of Code Bloat on Genetic Program Comprehension: Replication of a Controlled Experiment on Semantic Inference

Author

Listed:
  • Tomaž Kosar

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

  • Boštjan Slivnik

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

Abstract

Our previous study showed that automatically generated attribute grammars were harder to comprehend than manually written attribute grammars, mostly due to unexpected solutions. This study is an internally differentiated replication of the previous experiment, but, unlike the previous one, it focused on testing the influence of code bloat on comprehension correctness and efficiency. While the experiment’s context, design, and measurements were kept mostly the same as in the original experiment, more realistic code bloat examples were introduced. The replicated experiment was conducted with undergraduate students from two universities, showing statistically significant differences in comprehension correctness and efficiency between attribute grammars without code bloat and attribute grammars with code bloat, although the participants perceived attribute grammars with code bloat as simple as attribute grammars without code bloat. On the other hand, there was no statistically significant difference in comprehension correctness and efficiency between automatically generated attribute grammars with possible unexpected solutions and attribute grammars with code bloat, although there was a statistically significant difference in participants’ perspective of simplicity between automatically generated attribute grammars with possible unexpected solutions and attribute grammars with code bloat. The participants perceived attribute grammars with code bloat as significantly simpler than automatically generated attribute grammars.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3744-:d:1229816
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    References listed on IDEAS

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    1. Alhussein Fawzi & Matej Balog & Aja Huang & Thomas Hubert & Bernardino Romera-Paredes & Mohammadamin Barekatain & Alexander Novikov & Francisco J. R. Ruiz & Julian Schrittwieser & Grzegorz Swirszcz & , 2022. "Discovering faster matrix multiplication algorithms with reinforcement learning," Nature, Nature, vol. 610(7930), pages 47-53, October.
    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.
    3. 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.
    4. Sadik Arslan & Mert Ozkaya & Geylani Kardas, 2023. "Modeling Languages for Internet of Things (IoT) Applications: A Comparative Analysis Study," Mathematics, MDPI, vol. 11(5), pages 1-35, March.
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