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Lov Kumar¹, Sahiti Tummalapalli², Lalita Bhanu Murthy²

¹NIT Kurukshetra, Kurukshetra, Haryana, India. lovkumar505@gmail.com.

Scientific reports

|February 12, 2025

概括

这项研究开发了一个网络服务反模式预测模型,成功解决了类不平衡和特征冗余. 带有RBF内核的最小方位支向量机 (LSSVM) 实现了最佳性能,提高了预测准确性和模型质量.

科学领域:

软件工程软件工程软件工程
机器学习机器学习
网页服务网页服务

背景情况:

软件设计中的反模式违反了原则,对质量和可维护性产生了负面影响.
早期预测和重构反模式对于改善软件执行成本,维护成本和内存消耗至关重要.
现有的研究仅限于在反模式预测模型中共同解决阶级不平衡和特征冗余问题.

研究的目的:

使用预处理的软件源代码指标构建一个Web服务反模式预测模型.
通过处理不平衡的数据和特征冗余来提高模型性能和预测准确性.
对不同的采样和特征选择策略进行全面的比较分析.

主要方法:

从预处理的源代码中使用聚合措施提取系统级指标.
应用各种特征选择技术来识别和删除不相关的特征.
利用各种数据采样技术来缓解阶级不平衡.
使用精度和ROC曲线下的面积 (AUC) 评估了33个不同的分类器.

主要成果:

带有 RBF 内核的最小正方形支向量机 (LSSVM) 在 33 个分类器中表现出优越的性能,达到最低的弗里德曼平均等级 (1.18).
使用显著特征开发的模型显示,与其他特征选择技术相比,平均准确度 (88.40%) 和平均AUC (0.88) 更高.
提升采样方法 (UPSAM) 实现了最高的平均精度 (86.14%) 和平均AUC (0.87).
在采样和特征选择策略应用后,模型性能显著改善,AUC值从0.805到0.99不等.

结论:

类不平衡和特征不相关性会对Web服务反模式预测模型的性能产生不利影响.
采样和特征选择策略的应用对于提高预测准确性和模型质量至关重要.
带有RBF内核的LSSVM,结合有效的特征选择和采样,为Web服务反模式预测提供了一个强大的解决方案.

关键词:

聚合措施聚合措施这是一个反模式.数据采样数据采样特性选择技术特性选择技术机器学习是机器学习.

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