Exploring Large Language Models for Code Explanation
This addresses the problem of automating code documentation for software engineers, but it is incremental as it builds on existing LLM applications in software engineering.
The study tackled generating natural-language summaries for code snippets using large language models, finding that Code LLMs outperform generic ones and zero-shot methods work better with datasets having dissimilar training-testing distributions.
Automating code documentation through explanatory text can prove highly beneficial in code understanding. Large Language Models (LLMs) have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically delves into 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.