IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i17p3744-d1229816.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/17/3744/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/17/3744/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    2. O’Malley, Cormac & de Mars, Patrick & Badesa, Luis & Strbac, Goran, 2023. "Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation," Applied Energy, Elsevier, vol. 349(C).
    3. Wentao Zhang & Yilei Zhao & Shuo Sun & Jie Ying & Yonggang Xie & Zitao Song & Xinrun Wang & Bo An, 2023. "Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools," Papers 2311.10801, arXiv.org, revised Feb 2024.
    4. El Amine Cherrat & Snehal Raj & Iordanis Kerenidis & Abhishek Shekhar & Ben Wood & Jon Dee & Shouvanik Chakrabarti & Richard Chen & Dylan Herman & Shaohan Hu & Pierre Minssen & Ruslan Shaydulin & Yue , 2023. "Quantum Deep Hedging," Papers 2303.16585, arXiv.org, revised Nov 2023.
    5. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3744-:d:1229816. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.