一句话：提出用深度类属性图和图神经网络自动识别提取类重构机会，以降低对专家度量的依赖并提升建议精度与适应性。
提取类重构用于分解大型复杂类，从而提升代码可维护性与可读性，但传统重构工具主要依赖内聚度、耦合度等度量，往往需要专家判断且难以普遍适用。本文提出一种基于深度类属性图与先进图神经网络的方法，通过融合深层语义属性和细粒度结构依赖，自动识别适合执行提取类重构的类。该方法旨在减少对专家知识的依赖，并提高重构建议的精确性与适应性。摘要未报告具体实验评估；未来工作将探索超图学习，以刻画更复杂的代码关系并进一步增强方法效果。
Extract Class refactoring is essential for decomposing large, complex classes to improve code maintainability and readability. Traditional refactoring tools rely heavily on metrics like cohesion and coupling, which often require expert judgment and are not universally applicable. This research proposes a novel approach leveraging deep class property graphs and advanced graph neural networks to automate the identification of refactoring opportunities. By integrating deep semantic properties and fine-grained structural dependencies, this method aims to reduce reliance on expert knowledge and improve the precision and adaptability of refactoring suggestions. Future work will explore hypergraph learning to capture more complex code relationships, further enhancing the proposed method's effectiveness.
DOI 原文 ·
@inproceedings{paperbot8539,
title = {Graph Learning for Extract Class Refactoring},
author = {Luqiao Wang},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3685561}
}