StatsClaw：统计软件开发的AI协作工作流
StatsClaw: An AI-Collaborative Workflow for Statistical Software Development
AI总结：
StatsClaw通过多智能体架构实现统计软件开发的可靠自动化，通过信息隔离机制确保代码生成与验证的独立性，提升软件生命周期的工程效率并保持研究人员对方法学的控制。
摘要：
将统计方法转化为可靠软件是量化研究中的持续瓶颈。现有AI代码生成工具虽然能快速生成代码，但无法保证实现的准确性，这对统计软件至关重要。我们介绍了StatsClaw，一种为Claude Code设计的多智能体架构，通过在代码生成和验证之间建立信息壁垒。规划代理生成独立的实现、模拟和测试规范，并将其分配给无法看到彼此指令的分离代理：构建代理在不知道真实参数的情况下进行实现，模拟代理在不知道算法的情况下生成数据，测试代理使用确定性标准进行验证。我们描述了该方法，展示了其在概率估计包中的端到端应用，并在作者自己的R和Python包上的三个应用中进行了评估。结果表明，结构化的AI辅助工作流能够吸收软件生命周期的工程开销，同时保持研究人员对每个实质性方法学决策的控制。
英文摘要：
Translating statistical methods into reliable software is a persistent bottleneck in quantitative research. Existing AI code-generation tools produce code quickly but cannot guarantee faithful implementation -- a critical requirement for statistical software. We introduce StatsClaw, a multi-agent architecture for Claude Code that enforces information barriers between code generation and validation. A planning agent produces independent specifications for implementation, simulation, and testing, dispatching them to separate agents that cannot see each other's instructions: the builder implements without knowing the ground-truth parameters, the simulator generates data without knowing the algorithm, and the tester validates using deterministic criteria. We describe the approach, demonstrate it end-to-end on a probit estimation package, and evaluate it across three applications to the authors' own R and Python packages. The results show that structured AI-assisted workflows can absorb the engineering overhead of the software lifecycle while preserving researcher control over every substantive methodological decision.
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。
