  This paper presents a comprehensive comparative analysis of Large Language
Models (LLMs) for generation of code documentation. Code documentation is an
essential part of the software writing process. The paper evaluates models such
as GPT-3.5, GPT-4, Bard, Llama2, and Starchat on various parameters like
Accuracy, Completeness, Relevance, Understandability, Readability and Time
Taken for different levels of code documentation. Our evaluation employs a
checklist-based system to minimize subjectivity, providing a more objective
assessment. We find that, barring Starchat, all LLMs consistently outperform
the original documentation. Notably, closed-source models GPT-3.5, GPT-4, and
Bard exhibit superior performance across various parameters compared to
open-source/source-available LLMs, namely LLama 2 and StarChat. Considering the
time taken for generation, GPT-4 demonstrated the longest duration, followed by
Llama2, Bard, with ChatGPT and Starchat having comparable generation times.
Additionally, file level documentation had a considerably worse performance
across all parameters (except for time taken) as compared to inline and
function level documentation.
