Analyzing surrounding code, such as class attributes or imported types, to provide the model with necessary context.
The methodology for automating this process has shifted through three distinct phases: Automated Docstring Generation for Python Funct...
Despite significant progress, automated generation faces critical hurdles. remains the primary risk, where a model may confidently describe a side effect or exception that does not exist in the code. Furthermore, "Stale Documentation" occurs when code is updated but the automated pipeline is not re-triggered, leading to a mismatch between docstrings and implementation. Conclusion Analyzing surrounding code, such as class attributes or
Early tools relied on static analysis to pull function names and argument lists, providing a boilerplate structure (e.g., :param x: ) that still required manual completion. providing a boilerplate structure (e.g.
Constructing instructions that specify the desired format (e.g., "Generate a NumPy-style docstring for the following Python function").
Modern automated pipelines typically follow a four-step process: