The why, what, and how of AI-based coding in scientific research
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 3 Oct 2024.
Builds on
9 of 21 references.
References · 9
- 2024A Survey on Large Language Models for Code Generationcited by 646
- 2024DebugBench: Evaluating Debugging Capability of Large Language Modelscited by 74
- 2023Large Language Models for Compiler Optimizationcited by 52
- 2023No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPTcited by 45
- 2023AI-assisted coding: Experiments with GPT-4cited by 43
- 2023LLMs for Science: Usage for Code Generation and Data Analysiscited by 34
- 2023Techniques for supercharging academic writing with generative AIcited by 8
- 2024Beyond principlism: Practical strategies for ethical AI use in research practicescited by 7
- 2024An Exploratory Study on Upper-Level Computing Students' Use of Large Language Models as Tools in a Semester-Long Projectcited by 6
Built on by
Not cited yet.