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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07450 |
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| _version_ | 1866912427970396160 |
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| author | Yadav, Sargam Qureshi, Asifa Mehmood Kaushik, Abhishek Sharma, Shubham Loughran, Roisin Kazhuparambil, Subramaniam Shaw, Andrew Sabry, Mohammed Lynch, Niamh St John Singh, . Nikhil O'Hara, Padraic Jaiswal, Pranay Chandru, Roshan Lillis, David |
| author_facet | Yadav, Sargam Qureshi, Asifa Mehmood Kaushik, Abhishek Sharma, Shubham Loughran, Roisin Kazhuparambil, Subramaniam Shaw, Andrew Sabry, Mohammed Lynch, Niamh St John Singh, . Nikhil O'Hara, Padraic Jaiswal, Pranay Chandru, Roshan Lillis, David |
| contents | 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 Pre-Trained 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 analysed 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 increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper Yadav, Sargam Qureshi, Asifa Mehmood Kaushik, Abhishek Sharma, Shubham Loughran, Roisin Kazhuparambil, Subramaniam Shaw, Andrew Sabry, Mohammed Lynch, Niamh St John Singh, . Nikhil O'Hara, Padraic Jaiswal, Pranay Chandru, Roshan Lillis, David Artificial Intelligence 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 Pre-Trained 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 analysed 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 increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted. |
| title | From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.07450 |