I Automated Python Docstrings for a Month — The Workflow That Finally Stuck

说明与用途

Python 文档字符串自动化：使用标准库审计工具与 AI 生成器结合，并通过人工审核和 doctest 验证。

问题

Python developers struggle to keep docstrings current and accurate, leading to stale or incorrect documentation that misleads readers.

结果

The combined workflow reduced manual docstring writing, increased coverage, and produced more specific documentation than hand‑written versions, though occasional hallucinations required human review.

工作流

• Audit the codebase for missing or stale docstrings using a stdlib script or coverage report.
• Generate missing docstrings with an AI doc generator that outputs Google‑style docstrings.
• Verify the generated docstrings with a human skim and run doctests on critical functions.
• Explain the process and rules to AI coding agents to improve adherence.
• Iterate the loop on every PR, using coverage numbers as a gate.

证据

I ran a stdlib coverage audit for the baseline, let three tools generate docstrings for the undocumented remainder, then gave a separate AI model twenty randomly chosen docstrings …

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My hand‑written docstrings did not win. Three of the five lowest‑scoring entries were mine …

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One hallucination. A generator invented a keyword argument that does not exist.

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