Neural Code Summarization

TL;DR

This paper presents an automatic neural approach for code summarization that generates meaningful descriptions of software, aiding program comprehension and reducing manual effort in updating code documentation.

Contribution

It introduces a neural code summarization method evaluated on benchmarked and custom datasets, highlighting its effectiveness in generating accurate code descriptions.

Findings

Effective automatic code summaries generated

Comparable or improved over manual descriptions

Applicable to evolving codebases

Abstract

Code summarization is the task of generating readable summaries that are semantically meaningful and can accurately describe the presumed task of a software. Program comprehension has become one of the most tedious tasks for knowledge transfer. As the codebase evolves over time, the description needs to be manually updated each time with the changes made. An automatic approach is proposed to infer such captions based on benchmarked and custom datasets with comparison between the original and generated results.