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ALSI-Transformer: Transformer-Based Code Comment Generation with Aligned Lexical and Syntactic Informationopen access

Authors
YOUNGMI PARKAHJEONG PARKCHULYUN KIM
Issue Date
Apr-2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Codes; comment generation; Data mining; deep learning; Logic gates; Machine translation; natural language processing; program comprehension; Source coding; Syntactics; Transformers
Citation
IEEE Access, v.11, pp 39037 - 39047
Pages
11
Journal Title
IEEE Access
Volume
11
Start Page
39037
End Page
39047
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/151941
DOI
10.1109/ACCESS.2023.3268638
ISSN
2169-3536
2169-3536
Abstract
Code comments explain the operational process of a computer program and increase the long-term productivity of programming tasks such as debugging and maintenance. Therefore, developing methods that automatically generate natural language comments from programming code is required. With the development of deep learning, various excellent models in the natural language processing domain have been applied for comment generation tasks, and recent studies have improved performance by simultaneously using the lexical information of the code token and the syntactical information obtained from the syntax tree. In this paper, to improve the accuracy of automatic comment generation, we introduce a novel syntactic sequence, Code-Aligned Type sequence (CAT), to align the order and length of lexical and syntactic information, and we propose a new neural network model, Aligned Lexical and Syntactic information-Transformer (ALSI-Transformer), based on a transformer that encodes the aligned multi-modal information with convolution and embedding aggregation layers. Through in-depth experiments, we compared ALSI-Transformer with current baseline methods using standard machine translation metrics and demonstrate that the proposed method achieves state-of-the-art performance in code comment generation. Author
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공과대학 (인공지능공학부)
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