Automated Docstring Generation | For Python Funct...

This paper examines the evolution and implementation of automated docstring generation for Python functions, focusing on how Large Language Models (LLMs) have transformed documentation from a manual burden into an integrated part of the development lifecycle. The Role of Docstrings in Python

Modern automated pipelines typically follow a four-step process: Automated Docstring Generation for Python Funct...

Constructing instructions that specify the desired format (e.g., "Generate a NumPy-style docstring for the following Python function"). This paper examines the evolution and implementation of

In Python, docstrings serve as the primary source of truth for function behavior, parameters, and return types. Beyond mere commentary, they are programmatically accessible via the __doc__ attribute and power essential tools like Sphinx, Pydoc, and integrated development environment (IDE) tooltips. However, the "documentation debt" remains high in many projects, as developers often prioritize feature delivery over descriptive prose. Evolution of Automation Techniques Early tools relied on static analysis to pull

Tools like Pyment attempted to "translate" between different docstring formats (Google, NumPy, Epytext) but struggled to interpret the actual logic of the code.

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.