2025/11/10 by Hanya Elhashemy, Elhashemy, Hanya, Yong‐Jian Tang +2 · 1 citation
Computer Science · Decision Sciences · #Software Engineering Research #Scientific Computing and Data Management #Machine Learning and Data Classification
paper · pdf · doi:10.48550/arxiv.2511.07257
The increasing adoption of Jupyter notebooks in data science and machine learning workflows has created a gap between exploratory code development and production-ready software systems. While notebooks excel at iterative development and visualization, they often lack proper software engineering principles, making their transition to production environments challenging. This paper presents Codelevate, a novel multi-agent system that automatically transforms Jupyter notebooks into well-structured, maintainable Python code repositories. Our system employs three specialized agents - Architect, Developer, and Structure - working in concert through a shared dependency tree to ensure architectural coherence and code quality. Our experimental results validate Codelevate's capability to bridge the prototype-to-production gap through autonomous code transformation, yielding quantifiable improvements in code quality metrics while preserving computational semantics.