CodePod：一种面向交互式开发的语言无关分层作用域系统
CodePod: A Language-Agnostic Hierarchical Scoping System for Interactive Development
AI总结：
针对Jupyter Notebook线性结构与全局命名空间限制大型项目扩展性的问题，提出语言无关的分层作用域系统CodePod，通过源码级转换实现嵌套模块增量求值，经形式化证明与用户研究验证可显著提升项目可扩展性。
摘要：
Jupyter Notebooks 这类交互式开发环境通过代码单元实现增量编码并提供即时反馈，但其线性结构和全局命名空间限制了大型软件项目的可扩展性。我们提出 CodePod，这是 Jupyter 的分层扩展，引入了具备形式化语义的新型作用域执行模型。我们的核心贡献是一个语言无关的运行时系统，它通过源码级转换实现分层作用域规则，无需修改语言特定的内核，就能在嵌套模块间实现真正的增量求值。我们将作用域语义形式化为一个具有精确可见性关系的数学框架，并证明了符号解析唯一性、解析算法正确性等关键性质。一项由7名资深开发者参与的定性用户研究表明，与 Jupyter 相比，CodePod 显著提升了项目可扩展性，导航工作量明显减少。我们在拥有数千行代码的大型项目上验证了该系统的有效性，证明其适用性超越了传统笔记本的边界。我们的工具是开源的，可通过 https://codepod.io 获取。
英文摘要：
Interactive development environments like Jupyter Notebooks enable incremental coding through cells with immediate feedback, but their linear structure and global namespace limit scalability for large software projects. We present CodePod, a hierarchical extension of Jupyter that introduces a novel scoped execution model with formal semantics. Our key contribution is a language-agnostic runtime system that performs source-level transformations to implement hierarchical scoping rules, enabling true incremental evaluation across nested modules without requiring language-specific kernel modifications. We formalize the scoping semantics as a mathematical framework with precise visibility relations and prove key properties including uniqueness of symbol resolution and correctness of the resolution algorithm. A qualitative user study with seven senior developers demonstrates that CodePod enables significant improvements in project scalability compared to Jupyter, with notable reductions in navigation effort. We validate the system's effectiveness on large-scale projects with thousands of lines of code, demonstrating its applicability beyond traditional notebook boundaries. Our tool is open-source and available at https://codepod.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
