Automatic Code Summarization: A Systematic Literature Review
Yuxiang Zhu, Minxue Pan

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…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Software Engineering Research
