Re-opening open-source science through AI assisted development
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909959326793728 |
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| author | Hung, Ling-Hong Yeung, Ka Yee |
| author_facet | Hung, Ling-Hong Yeung, Ka Yee |
| contents | Open-source scientific software is effectively closed to modification by its complexity. With recent advances in technology, an agentic AI team led by a single human can now rapidly and robustly modify large codebases and re-open science to the community which can review and vet the AI generated code. We demonstrate this with a case study, STAR-Flex, which is an open source fork of STAR, adding 16,000 lines of C++ code to add the ability to process 10x Flex data, while maintaining full original function. This is the first open-source processing software for Flex data and was written as part of the NIH funded MorPHiC consortium. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11993 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Re-opening open-source science through AI assisted development Hung, Ling-Hong Yeung, Ka Yee Software Engineering Open-source scientific software is effectively closed to modification by its complexity. With recent advances in technology, an agentic AI team led by a single human can now rapidly and robustly modify large codebases and re-open science to the community which can review and vet the AI generated code. We demonstrate this with a case study, STAR-Flex, which is an open source fork of STAR, adding 16,000 lines of C++ code to add the ability to process 10x Flex data, while maintaining full original function. This is the first open-source processing software for Flex data and was written as part of the NIH funded MorPHiC consortium. |
| title | Re-opening open-source science through AI assisted development |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2512.11993 |