Leveraging Large Language Model to Assist Detecting Rust Code Comment Inconsistency

Rust is renowned for its robust memory safety capabilities, yet its distinctive memory management model poses substantial challenges in both writing and understanding programs. Within Rust source code, comments are employed to clearly delineate conditions that might cause panic behavior, thereby warning developers about potential hazards associated with specific operations. Therefore, comments are particularly crucial for documenting Rust's program logic and design. Nevertheless, as modern software frequently undergoes updates and modifications, maintaining the accuracy and relevance of these comments becomes a labor-intensive endeavor.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1145/3691620.3695010
Citations
4
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
1.39
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article

Leveraging Large Language Model to Assist Detecting Rust Code Comment Inconsistency

Yang Feng, Baowen Xu, Zixi Liu, Yichi Zhang
4 citations
Hate Speech and Cyberbullying Detection
1.39
article

Leveraging Large Language Model to Assist Detecting Rust Code Comment Inconsistency

Yang Feng, Baowen Xu, Zixi Liu, Yichi Zhang
article en
4 citations

Abstract

Rust is renowned for its robust memory safety capabilities, yet its distinctive memory management model poses substantial challenges in both writing and understanding programs. Within Rust source code, comments are employed to clearly delineate conditions that might cause panic behavior, thereby warning developers about potential hazards associated with specific operations. Therefore, comments are particularly crucial for documenting Rust's program logic and design. Nevertheless, as modern software frequently undergoes updates and modifications, maintaining the accuracy and relevance of these comments becomes a labor-intensive endeavor.

Nanjing University (CN)
Openalex Percentile: Top 15%
Hate Speech and Cyberbullying Detection
1.39
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