动态堆叠组合用于跨语言代码嗅觉检测
1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
PeerJ. Computer science
|September 24, 2024
概括
本研究介绍了用于在Java和Python中检测代码气味的动态组合,实现与完整堆叠组合相比的精度,但复杂性降低. 这些方法为识别软件设计问题提供了更稳定,更有效的方法.
科学领域:
- 软件工程 软件工程 软件工程
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 代码气味是软件设计和实施不良的指标.
- 对于代码嗅觉的机器学习是一个活跃的研究领域,但模型往往缺乏稳定性,主要集中在Java上.
- 现有的代码嗅觉检测方法可能很复杂,在编程语言中可能无法很好地泛化.
研究的目的:
- 在Java和Python中提出用于代码嗅觉的动态集合方法.
- 调查贪搜索和落后淘汰策略在构建这些动态乐团的有效性.
- 将动态组合的复杂性和检测性能与完整堆叠组合进行比较.
主要方法:
- 使用贪搜索和落后淘汰策略开发动态集合模型.
- 在四个Java和两个Python代码气味上的检测性能评估.
- 对模型复杂度和检测准确度的比较分析,使用完整的堆叠组合.
主要成果:
- 贪的搜索和向后消除为动态合奏产生了不同的基本模型集.
- 动态组合的检测性能与完整堆叠组合的检测性能相当,没有显著的损失.
- 动态组合,特别是那些使用逆向消除的组合,导致大多数研究的代码气味的模型不那么复杂.
结论:
- 动态堆叠合集提供了一种有效和稳定的方法来检测Java和Python代码的气味.
- 这些动态组合提供了减少复杂性的替代方案,而不是完整的堆叠组合.
- 拟议的策略有助于在多种编程语言中改进代码嗅觉检测.
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