Future of software development with generative AI
Sauvola, Jaakko; Tarkoma, Sasu; Klemettinen, Mika; Riekki, Jukka; Doermann, David (2024-03-11)
Springer
11.03.2024
Sauvola, J., Tarkoma, S., Klemettinen, M. et al. Future of software development with generative AI. Autom Softw Eng 31, 26 (2024). https://doi.org/10.1007/s10515-024-00426-z
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© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403132215
https://urn.fi/URN:NBN:fi:oulu-202403132215
Tiivistelmä
Abstract
Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be used for many purposes, from creative dimensions to replacing repetitive and manual tasks. The number of opportunities increases with the capabilities of large-language models (LLMs). This has raised concerns about ethics, education, regulation, intellectual property, and even criminal activities. We analyzed the potential of generative AI and LLM technologies for future software development paths. We propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations. The motivation for this research is clear: the software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it.
Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be used for many purposes, from creative dimensions to replacing repetitive and manual tasks. The number of opportunities increases with the capabilities of large-language models (LLMs). This has raised concerns about ethics, education, regulation, intellectual property, and even criminal activities. We analyzed the potential of generative AI and LLM technologies for future software development paths. We propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations. The motivation for this research is clear: the software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it.
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