一句话：提出借助大语言模型辅助检测 Rust 代码注释不一致，以减轻注释维护负担。
Rust 以内存安全著称，但其独特的记忆管理模型使程序编写与理解面临挑战。Rust 源码中的注释常用于说明可能触发 panic 的条件，并警告特定操作的风险，因此对记录程序逻辑与设计尤为关键。然而现代软件频繁更新修改，保持注释准确且相关成为劳动密集型工作。本文据此提出利用大语言模型辅助检测 Rust 代码注释不一致。所给摘要仅说明研究动机与注释重要性，未展开具体方法、数据集、指标或实验结果。
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.
DOI 原文 ·
@inproceedings{paperbot8330,
title = {Leveraging Large Language Model to Assist Detecting Rust Code Comment Inconsistency},
author = {Yichi Zhang and Zixi Liu and Yang Feng and Baowen Xu},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695010}
}