Extended Abstract of SeCNN: A semantic CNN parser for code comment generation

Zheng Li; Yonghao Wu; Bin Peng; Xiang Chen; Zeyu Sun; Yong Liu; Deli Yu

doi:10.1109/SANER56733.2023.00099

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Conference proceeding

Extended Abstract of SeCNN: A semantic CNN parser for code comment generation

Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu and Deli Yu

Proceedings of the European Conference on Software Maintenance and Reengineering, pp.848-849

03/2023

DOI: https://doi.org/10.1109/SANER56733.2023.00099

Abstract

Codes

Maintenance engineering

Natural languages

Semantics

Software

Writing

Code comments are essential for software development and maintenance, as they provide natural language descriptions of the code that help developers understand the program and reduce the time spent on comprehension. However, writing code comments can be tedious and time-consuming, and many software projects lack comprehensive and up-to-date comments, which can impair the readability and maintainability of programs.

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Title: Extended Abstract of SeCNN: A semantic CNN parser for code comment generation
Creators - without role: Zheng Li - Beijing University of Chemical Technology
Yonghao Wu - Beijing University of Chemical Technology
Bin Peng - Beijing University of Chemical Technology
Xiang Chen - Nantong University
Zeyu Sun - Peking University
Yong Liu - Beijing University of Chemical Technology,College of Information Science and Technology,Beijing,China
Deli Yu - Beijing University of Chemical Technology
Publication Details: Proceedings of the European Conference on Software Maintenance and Reengineering, pp.848-849
Publisher: IEEE
Number of pages: 2
Identifiers: 9912428009846
Academic Unit: ESD Pillar
Language: English
Resource Type: Conference proceeding

Extended Abstract of SeCNN: A semantic CNN parser for code comment generation

Abstract

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