Exploring Large Language Models for Code Explanation

Automating code documentation through explanatory text can prove highly beneficial in code understanding. LLMs have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically explore the task of generating natural-language summaries for code snippets, using various LLMs. The findings indicate that Code LLMs outperform their generic counterparts, and zero-shot methods yield superior results when dealing with datasets with dissimilar distributions between training and testing sets.