StatsClaw: An AI-Collaborative Workflow for Statistical Software Development
This addresses the bottleneck of reliable software translation for quantitative researchers, though it is incremental as it builds on existing AI code-generation tools.
The paper tackles the problem of ensuring faithful implementation in statistical software development by introducing StatsClaw, a multi-agent AI workflow that enforces information barriers between code generation and validation, demonstrating it on a probit estimation package and evaluating it across three R and Python applications.
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.