A Comprehensive Survey on Code Summarization

A thorough understanding of the program code is required for software development and maintenance projects. Although well-written comments are crucial for supporting this understanding, modern software frequently lacks them or has out-of-date versions. Automatic code summarization has surfaced as a viable solution to this problem. Even though this field has advanced significantly in the past ten years, there is still a noticeable lack of a current survey that compiles the most recent developments. This study classifies the articles based on using Abstract Syntax Trees (AST) in code summarization. It conducts a comprehensive evaluation of several code summarization research articles that employ AST and those that do not. The paper explores each of them based on state-of-the-art techniques. Finally, a brief overview of evaluation metrics for code summarization is provided.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/icpids65698.2024.00074
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
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A Comprehensive Survey on Code Summarization

S.N. Gunjal, Ritwik Kulkarni, Hrishikesh Kadival, Rimzim Khinchi et al.
Natural Language Processing Techniques
article

A Comprehensive Survey on Code Summarization

S.N. Gunjal, Ritwik Kulkarni, Hrishikesh Kadival, Rimzim Khinchi, Piyush Kothekar
article en

Abstract

A thorough understanding of the program code is required for software development and maintenance projects. Although well-written comments are crucial for supporting this understanding, modern software frequently lacks them or has out-of-date versions. Automatic code summarization has surfaced as a viable solution to this problem. Even though this field has advanced significantly in the past ten years, there is still a noticeable lack of a current survey that compiles the most recent developments. This study classifies the articles based on using Abstract Syntax Trees (AST) in code summarization. It conducts a comprehensive evaluation of several code summarization research articles that employ AST and those that do not. The paper explores each of them based on state-of-the-art techniques. Finally, a brief overview of evaluation metrics for code summarization is provided.

Openalex Percentile: Top 73%
Natural Language Processing Techniques
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