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Large Language Model Assisted Software Engineering: Prospects, Challenges, and a Case Study

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Bridging the Gap Between AI and Reality (AISoLA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14380))

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Abstract

Large language models such as OpenAI’s GPT and Google’s Bard offer new opportunities for supporting software engineering processes. Large language model assisted software engineering promises to support developers in a conversational way with expert knowledge over the whole software lifecycle. Current applications range from requirements extraction, ambiguity resolution, code and test case generation, code review and translation to verification and repair of software vulnerabilities. In this paper we present our position on the potential benefits and challenges associated with the adoption of language models in software engineering. In particular, we focus on the possible applications of large language models for requirements engineering, system design, code and test generation, code quality reviews, and software process management. We also give a short review of the state-of-the-art of large language model support for software construction and illustrate our position by a case study on the object-oriented development of a simple “search and rescue” scenario.

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Notes

  1. 1.

    https://github.com/mermaid-js/mermaid.

  2. 2.

    ChatGPT https://chat.openai.com/share/a93d844d-e542-4997-a7d5-0d254e007c08.

  3. 3.

    Bard https://g.co/bard/share/c51838296a3c.

  4. 4.

    https://github.com/Significant-Gravitas/Auto-GPT.

  5. 5.

    https://github.com/geekan/MetaGPT.

  6. 6.

    https://github.com/RoboCoachTechnologies/GPT-Synthesizer.

  7. 7.

    https://github.com/AntonOsika/gpt-engineer.

  8. 8.

    Despite the validity of the Church–Turing thesis, more powerful tools enable more products in practice.

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Acknowledgements

We thank the anonymous reviewer for constructive criticisms and helpful suggestions.

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Authors and Affiliations

  1. TH Ingolstadt, Ingolstadt, Germany

    Lenz Belzner

  2. Ludwig-Maximilians-Universität München, München, Germany

    Thomas Gabor & Martin Wirsing

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  1. Lenz Belzner
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Correspondence to Martin Wirsing .

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  1. TU Dortmund University, Dortmund, Germany

    Bernhard Steffen

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Belzner, L., Gabor, T., Wirsing, M. (2024). Large Language Model Assisted Software Engineering: Prospects, Challenges, and a Case Study. In: Steffen, B. (eds) Bridging the Gap Between AI and Reality. AISoLA 2023. Lecture Notes in Computer Science, vol 14380. Springer, Cham. https://doi.org/10.1007/978-3-031-46002-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-46002-9_23

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