用高质量合成数据提升代码注释分类效果
High-quality data augmentation for code comment classification
- 基于高质生成技术构建合成数据，解决注释数据少且不均衡问题
- 在NLBSE'26数据集上使分类器准确率提升2.56%
- 适合关注代码理解与数据增强的研究者
代码注释在软件开发中至关重要，用于记录功能、解释设计决策并辅助问题追踪。它们承载开发者对源码的洞察，是人机理解的重要资源。然而，由于注释使用自然语言，给机器理解带来挑战。近期研究采用自然语言处理和深度学习技术，按开发者意图对注释进行分类。但现有数据集受限于规模和类别不平衡，因依赖人工标注，难以真实反映实际代码库中的注释分布。为此，我们提出基于高质量生成的合成过采样与数据增强技术，用于改进NLBSE'26挑战赛数据集。所提出的合成质量过采样与增强技术（Q-SYNTH）表现优异，在基础分类器上实现2.56%的性能提升。
原文摘要 · Abstract (English)
Code comments serve a crucial role in software development for documenting functionality, clarifying design choices, and assisting with issue tracking. They capture developers' insights about the surrounding source code, serving as an essential resource for both human comprehension and automated analysis. Nevertheless, since comments are in natural language, they present challenges for machine-based code understanding. To address this, recent studies have applied natural language processing (NLP) and deep learning techniques to classify comments according to developers' intentions. However, existing datasets for this task suffer from size limitations and class imbalance, as they rely on manual annotations and may not accurately represent the distribution of comments in real-world codebases. To overcome this issue, we introduce new synthetic oversampling and augmentation techniques based on high-quality data generation to enhance the NLBSE'26 challenge datasets. Our Synthetic Quality Oversampling Technique and Augmentation Technique (Q-SYNTH) yield promising results, improving the base classifier by $2.56\%$.
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