Collaborative Research: FW-HTF-RM: AI-Assisted Programming: Equipping Social and Natural Scientists for the Future of Research

计算机编程对现代科学是必不可少的。科学家编写程序来控制仪器，运行模拟和分析数据。 然而，科学家使用的编程工具和技术往往落后于软件工程行业。这种滞后使得科学发现更慢，成本更高，并可能导致不可复制的结果。在过去的两年中，人工智能（AI）工具，如ChatGPT，已经彻底改变了软件行业。它们已经被证明可以使软件工程师的生产力显著提高，但对科学没有同样的影响。该研究项目的目标是开发和测试为科学家工作的AI编程工具。研究团队正在开发人工智能模型和工具，以支持科学家使用的编程语言。他们正在开发基准，以评估人工智能工具对科学特有的编程任务的有效性。他们正在研究人工智能编程工具如何帮助大学生更有效地学习科学。通过利用人工智能使科学家更容易编程，该项目有助于加速科学发现，降低成本，并允许更多的人参与科学工作。该项目正在开发大型代码语言模型和相关工具，以支持科学家。为了了解科学家的需求，该团队正在对科学家如何编写程序进行定性和定量研究。基于这些发现，他们正在为科学中经常使用的编程语言（如MATLAB和R）开发深度神经网络模型，但在软件工程行业中不太常用。 这些模型对于不是专业程序员的科学家特别有帮助;它们可以将描述转化为计算机程序，也可以生成对现有程序的解释。该团队正在开发支持科学家使用的编程范式的模型，包括计算笔记本和结构由数据格式决定的程序。该团队正在开发代码生成模型，可以部署在私有的“空气间隙”网络上，使其适用于在敏感领域工作的科学家，包括能源和国防。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Computer programming is essential for modern science. Scientists write programs to control instruments, run simulations, and analyze data. However, the programming tools and techniques that scientists use often lag behind those in the software engineering industry. This lag makes scientific discovery slower, more costly, and can lead to unreproducible results. In the past two years, artificial intelligence (AI) tools, such as ChatGPT, have revolutionized the software industry. They have been shown to make software engineers significantly more productive, but have not had the same impact on the sciences. The goal of this research project is to develop and test AI programming tools that work for scientists. The research team is developing AI models and tools that support the programming languages that scientists use. They are developing benchmarks to evaluate the effectiveness of AI tools for programming tasks that are unique to the sciences. They are investigating how AI programming tools can help college students study science more effectively. By harnessing AI to make programming easier for scientists, the project is helping to accelerate scientific discovery, lower its cost, and allow more people to participate in scientific work.The project is developing large language models of code and associated tools to support scientists. To understand scientists' needs, the team is running qualitative and quantitative studies of how scientists write programs. Based on these findings, they are developing deep neural network models for programming languages that are frequently used in the sciences, such as MATLAB and R, but are less commonly used in the software engineering industry. These models are particularly helpful for scientists who are not expert programmers; they can turn descriptions into computer programs, and also generate explanations of existing programs. The team is developing models that support the programming paradigms that scientists use, including computational notebooks and programs whose structure is determined by data formats. The team is developing code generation models that can be deployed on private, "air gapped" networks, making them suitable for scientists working in sensitive fields, including energy and defense.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.