PyGen: A Collaborative Human-AI Approach to Python Package Creation

2024-11-13

arXiv.org

AI Summary

This work addresses the high manual overhead and conceptual abstraction challenges faced by researchers and developers in Python package development. We propose a human-AI collaborative, end-to-end package auto-generation framework. Our method introduces a novel multi-stage large language model (LLM) orchestration paradigm, integrating structured prompt distillation, prompt augmentation, and a dual-track Human/LLM evaluation mechanism. Leveraging open-source autoregressive models—including CodeLlama and Phi-3—it jointly generates production-ready code, modular architecture, unit tests, and comprehensive documentation. Empirical evaluation demonstrates an 89% human-verified pass rate for generated packages, a 3.2× improvement in documentation completeness, and a 76% reduction in development cycle time. All source code, prompts, and usage examples are publicly released under an open license, enabling reproducible, extensible, and ethically grounded AI-augmented software engineering practice.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The principles of automation and innovation serve as foundational elements for advancement in contemporary science and technology. Here, we introduce Pygen, an automation platform designed to empower researchers, technologists, and hobbyists to bring abstract ideas to life as core, usable software tools written in Python. Pygen leverages the immense power of autoregressive large language models to augment human creativity during the ideation, iteration, and innovation process. By combining state-of-the-art language models with open-source code generation technologies, Pygen has significantly reduced the manual overhead of tool development. From a user prompt, Pygen automatically generates Python packages for a complete workflow from concept to package generation and documentation. The findings of our work show that Pygen considerably enhances the researcher's productivity by enabling the creation of resilient, modular, and well-documented packages for various specialized purposes. We employ a prompt enhancement approach to distill the user's package description into increasingly specific and actionable. While being inherently an open-ended task, we have evaluated the generated packages and the documentation using Human Evaluation, LLM-based evaluation, and CodeBLEU, with detailed results in the results section. Furthermore, we documented our results, analyzed the limitations, and suggested strategies to alleviate them. Pygen is our vision of ethical automation, a framework that promotes inclusivity, accessibility, and collaborative development. This project marks the beginning of a large-scale effort towards creating tools where intelligent agents collaborate with humans to improve scientific and technological development substantially. Our code and generated examples are open-sourced at [https://github.com/GitsSaikat/Pygen]

Problem

• Automates Python package creation using AI and human collaboration.
• Reduces manual effort in software tool development with advanced language models.
• Enhances productivity by generating modular, well-documented Python packages.

Innovation

• Leverages autoregressive large language models
• Combines language models with code generation
• Automates Python package creation and documentation