Large Language Models for Code Summarization

TL;DR

This paper reviews the performance of large language models in code summarization and generation, highlighting their effectiveness in explaining and generating code from natural language descriptions.

Contribution

It provides a comprehensive review of recent large language models' capabilities in code summarization and generation tasks, comparing their performance and potential.

Findings

Large language models perform well in code explanation tasks.

They show promising results in code generation from natural language.

The review highlights strengths and limitations of current models.

Abstract

Recently, there has been increasing activity in using deep learning for software engineering, including tasks like code generation and summarization. In particular, the most recent coding Large Language Models seem to perform well on these problems. In this technical report, we aim to review how these models perform in code explanation/summarization, while also investigating their code generation capabilities (based on natural language descriptions).