Code smell is a great challenge in software refactoring, which indicates latent design or implementation flaws that may degrade the software maintainability and evolution. Over the past decades, a variety of refactoring approaches have been proposed, which can be broadly classified into metrics-based, rule-based, and machine learning-based approaches. Recent years, deep learning-based approaches have also attracted widespread attention. However, existing techniques exhibit various limitations. Metrics- and rule-based approaches rely heavily on manually defined heuristics and thresholds, whereas deep learning-based approaches are often constrained by dataset availability and model design. In this study, we proposed a graph-based deep learning approach for code smell refactoring. Specifically, we designed two types of input graphs (class-level and method-level) and employed both graph classification and node classification tasks to address the refactoring of three representative code smells: long method, large class, and feature envy. In our experiment, we propose a semi-automated dataset generation approach that could generate a large-scale dataset with minimal manual effort. We implemented the proposed approach with three classical GNN (graph neural network) architectures: GCN, GraphSAGE, and GAT, and evaluated its performance against both traditional and state-of-the-art deep learning approaches. The results demonstrate that proposed approach achieves superior refactoring performance.

翻译：代码异味是软件重构中的重大挑战，它指示了可能降低软件可维护性和可演化性的潜在设计或实现缺陷。过去几十年间，研究者提出了多种重构方法，可大致分为基于度量、基于规则和基于机器学习的方法。近年来，基于深度学习的方法也引起了广泛关注。然而，现有技术存在各种局限性：基于度量和规则的方法严重依赖人工定义的启发式规则和阈值，而基于深度学习的方法则常受限于数据集可用性和模型设计。在本研究中，我们提出了一种基于图的深度学习方法用于代码异味重构。具体而言，我们设计了两种类型的输入图（类级和方法级），并采用图分类和节点分类两种任务来处理三种代表性代码异味（长方法、大类、特征依恋）的重构。实验中，我们提出了一种半自动数据集生成方法，能够以最少的手动工作量生成大规模数据集。我们使用三种经典图神经网络架构（GCN、GraphSAGE和GAT）实现了所提出的方法，并与传统及最先进的深度学习方法进行了性能对比。结果表明，所提出的方法实现了更优的重构性能。