A Composable Just-In-Time Programming Framework with LLMs and FBP

• URL: http://arxiv.org/abs/2308.00204v1
• Date: Mon, 31 Jul 2023 23:51:46 GMT
• Title: A Composable Just-In-Time Programming Framework with LLMs and FBP
• Authors: Andy Vidan and Lars H. Fiedler
• Abstract summary: This paper introduces a computing framework that combines Flow-Based Programming (FBP) and Large Language Models (LLMs) to enable Just-In-Time Programming (JITP) JITP empowers users, regardless of their programming expertise, to actively participate in the development and automation process by leveraging their task-time algorithmic insights. The framework allows users to request and generate code in real-time, enabling dynamic code execution within a flow-based program.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a computing framework that combines Flow-Based Programming (FBP) and Large Language Models (LLMs) to enable Just-In-Time Programming (JITP). JITP empowers users, regardless of their programming expertise, to actively participate in the development and automation process by leveraging their task-time algorithmic insights. By seamlessly integrating LLMs into the FBP workflow, the framework allows users to request and generate code in real-time, enabling dynamic code execution within a flow-based program. The paper explores the motivations, principles, and benefits of JITP, showcasing its potential in automating tasks, orchestrating data workflows, and accelerating software development. Through a fully implemented JITP framework using the Composable platform, we explore several examples and use cases to illustrate the benefits of the framework in data engineering, data science and software development. The results demonstrate how the fusion of FBP and LLMs creates a powerful and user-centric computing paradigm.

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