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Using AI-Based Code Completion for Domain-Specific Languages

  • Conference paper
  • First Online:
Product-Focused Software Process Improvement (PROFES 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14483))

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Abstract

Code completion is a very important feature of modern integrated development environments. Research has been done for years to improve code completion systems for general-purpose languages. However, only little literature can be found for (AI-based) code completion for domain specific languages (DSLs). A DSL is a special-purpose programming language tailored for a specific application domain. In this paper, we investigate whether AI-based state-of-the-art code completion approaches can also be applied for DSLs. This is demonstrated using the domain-specific language TTI (Thermal Text Input). TTI is used for power transformer design specification in an industrial context, where an existing code completion shall be replaced by an advanced machine learning approach. For this purpose, implementations of two code completion systems are adapted to our needs. One of them shows very promising results and achieves a top-5 accuracy of 97%. To evaluate the practical applicability, the approach is integrated into an existing editor of a power transformer manufacturer.

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Notes

  1. 1.

    https://www.tabnine.com.

  2. 2.

    https://copilot.github.com.

  3. 3.

    https://github.com/features/copilot.

  4. 4.

    https://www.tabnine.com.

  5. 5.

    https://www.deepmind.com/blog/competitive-programming-with-alphacode.

  6. 6.

    https://aws.amazon.com/de/codewhisperer.

  7. 7.

    https://www.jetbrains.com/mps.

  8. 8.

    https://www.eclipse.org/Xtext.

  9. 9.

    https://www.rascal-mpl.org.

  10. 10.

    https://github.com/software-competence-center-hagenberg/DSL-Code-Completion.

  11. 11.

    https://github.com/motykatomasz/Pythia-AI-code-completion.

  12. 12.

    https://docs.ray.io/en/latest/tune/index.html.

  13. 13.

    https://github.com/Microsoft/onnxruntime.

  14. 14.

    https://github.com/nietras/OnnxSharp.

  15. 15.

    https://www.jetbrains.com/dotmemory.

  16. 16.

    https://www.jetbrains.com/profiler.

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Author information

Authors and Affiliations

  1. Software Competence Center Hagenberg GmbH, Hagenberg, Austria

    Christina Piereder, Verena Geist & Michael Moser

  2. Siemens Energy Austria GmbH, Wien, Austria

    Günter Fleck

  3. University of Applied Sciences Upper Austria, Hagenberg, Austria

    Josef Pichler

Authors
  1. Christina Piereder
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  2. Günter Fleck
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  3. Verena Geist
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  4. Michael Moser
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  5. Josef Pichler
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Corresponding author

Correspondence to Christina Piereder .

Editor information

Editors and Affiliations

  1. FHV Vorarlberg University of Applied Science, Dornbirn, Austria

    Regine Kadgien

  2. Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany

    Andreas Jedlitschka

  3. FHV Vorarlberg University of Applied Science, Dornbirn, Austria

    Andrea Janes

  4. University of Oulu, Oulu, Finland

    Valentina Lenarduzzi

  5. University of Oulu, Oulu, Finland

    Xiaozhou Li

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Piereder, C., Fleck, G., Geist, V., Moser, M., Pichler, J. (2024). Using AI-Based Code Completion for Domain-Specific Languages. In: Kadgien, R., Jedlitschka, A., Janes, A., Lenarduzzi, V., Li, X. (eds) Product-Focused Software Process Improvement. PROFES 2023. Lecture Notes in Computer Science, vol 14483. Springer, Cham. https://doi.org/10.1007/978-3-031-49266-2_16

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  • DOI: https://doi.org/10.1007/978-3-031-49266-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49265-5

  • Online ISBN: 978-3-031-49266-2

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