The why, what, and how of AI-based coding in scientific research

• URL: http://arxiv.org/abs/2410.02156v1
• Date: Thu, 3 Oct 2024 02:36:30 GMT
• Title: The why, what, and how of AI-based coding in scientific research
• Authors: Tonghe Zhuang, Zhicheng Lin,
• Abstract summary: Generative AI, particularly large language models (LLMs), has the potential to transform coding into intuitive conversations. We dissect AI-based coding through three key lenses. We address the limitations and future outlook of AI in coding.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Computer programming (coding) is indispensable for researchers across disciplines, yet it remains challenging to learn and time-consuming to carry out. Generative AI, particularly large language models (LLMs), has the potential to transform coding into intuitive conversations, but best practices and effective workflows are only emerging. We dissect AI-based coding through three key lenses: the nature and role of LLMs in coding (why), six types of coding assistance they provide (what), and a five-step workflow in action with practical implementation strategies (how). Additionally, we address the limitations and future outlook of AI in coding. By offering actionable insights, this framework helps to guide researchers in effectively leveraging AI to enhance coding practices and education, accelerating scientific progress.

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