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

The methodology for automating this process has shifted through three distinct phases:

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

Utilizing linters like pydocstyle or darglint to ensure the generated documentation matches the actual code signature. Challenges and Limitations

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