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臭味代码++:用于代码嗅觉检测的多标签数据集
Nawaf Alomari1, Amal Alazba2, Hamoud Aljamaan3,4
1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. g201931050@kfupm.edu.sa.
Scientific data
|July 12, 2025
概括
这项研究引入了一个新的多标签数据集用于代码嗅觉检测,提高了软件质量分析的现实性. 数据集支持先进的检测方法,提高维护能力和重构工作.
科学领域:
- 软件工程 软件工程 软件工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 代码气味意味着软件设计不佳,影响维护能力,需要准确的检测才能进行有效的重构.
- 当前的数据集经常使用单标签分类,这并不反映现实世界项目中代码嗅觉事件的复杂,多方面的性质.
研究的目的:
- 开发一种新的多标签数据集,用于代码嗅觉检测.
- 整合来自开源Java项目的文本和数字特征,以实现更现实的表示.
- 为了促进先进的研究和提高代码嗅觉检测工具的准确性.
主要方法:
- 从103个开源Java项目中收集了代码.
- 将代码解析成抽象语法树 (AST),并提取相关特征.
- 使用数据清理和统一技术,对四种特定代码气味 (God Class,Data Class,Feature Envy,Long Method) 进行注释样本.
主要成果:
- 创建了一个包含107,554个样本的数据集,具有多标签注释,增强检测现实性.
- 获得了高的F1分数:数据类的95.89%,上帝类的94.48%,特征嫉妒的88.68%,长方法的88.87%.
- 该数据集为评估和改进代码嗅觉检测算法提供了坚实的基础.
结论:
- 开发的数据集对于高级代码嗅觉检测研究非常有价值,包括微调大型语言模型 (LLM).
- 未来的工作可以扩展数据集,包括其他编程语言和额外的代码气味,增加其适用性和多样性.
- 该资源将通过更准确的代码气味识别,有助于提高软件质量和可维护性.
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