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Abstract

The introduction of transformer architecture was a turning point in natural language processing (NLP). Models based on the transformer architecture, such as bidirectional encoder representations from transformers (BERT) and generative pretrained transformer (GPT), have gained widespread popularity in various applications such as software development and education. The availability of large language models (LLMs) such as ChatGPT and Bard to the general public has showcased the tremendous potential of these models and encouraged their integration into various domains such as software development, for tasks such as code generation, debugging, and documentation generation. In this study, opinions from 11 experts regarding their experience with LLMs for software development have been gathered and analyzed to draw insights that can guide successful and responsible integration. The overall opinion of the experts is positive, with the experts identifying advantages such as an increase in productivity and reduced coding time. Potential concerns and challenges such as risk of overdependence and ethical considerations have also been highlighted.

Keywords

ChatGPT, coding, large language models, natural language generation, software development, transformer

Publication Date

2026-01-01

Publication Title

Applied AI Letters

Volume

7

Issue

3

Deposit Date

2026-07-27

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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