Automatic Code Summarization: A Systematic Literature Review

TL;DR

This paper systematically reviews 41 studies on automatic code summarization, providing a comprehensive overview of current approaches, evaluation methods, and future research directions in the field of program comprehension.

Contribution

It offers the first exhaustive systematic literature review on automatic code summarization, analyzing data extraction, description generation, and evaluation techniques.

Findings

Identified key data extraction and description generation methods.

Analyzed evaluation techniques used in the field.

Discussed future research directions and challenges.

Abstract

Background: During software maintenance and development, the comprehension of program code is key to success. High-quality comments can help us better understand programs, but they're often missing or outmoded in today's programs. Automatic code summarization is proposed to solve these problems. During the last decade, huge progress has been made in this field, but there is a lack of an up-to-date survey. Aims: We studied publications concerning code summarization in the field of program comprehension to investigate state-of-the-art approaches. By reading and analyzing relevant articles, we aim at obtaining a comprehensive understanding of the current status of automatic code summarization. Method: In this paper, we performed a systematic literature review over the automatic source code summarization field. Furthermore, we synthesized the obtained data and investigated different approaches. Results: We successfully collected and analyzed 41 selected studies from the different research communities. We exhaustively investigated and described the data extraction techniques, description generation methods, evaluation methods and relevant artifacts of those works. Conclusions: Our systematic review provides an overview of the state of the art, and we also discuss further research directions. By fully elaborating current approaches in the field, our work sheds light on future research directions of program comprehension and comment generation.