LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908414736596992 |
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| author | Belozerov, Vladislav Barclay, Peter J Sami, Askhan |
| author_facet | Belozerov, Vladislav Barclay, Peter J Sami, Askhan |
| contents | Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safety regulations, and add tests that catch sycophantic answers -- so they can gain speed without losing security and accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLMs in Coding and their Impact on the Commercial Software Engineering Landscape Belozerov, Vladislav Barclay, Peter J Sami, Askhan Software Engineering Artificial Intelligence Machine Learning Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safety regulations, and add tests that catch sycophantic answers -- so they can gain speed without losing security and accuracy. |
| title | LLMs in Coding and their Impact on the Commercial Software Engineering Landscape |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.16653 |