相关实验视频
关于网络指标对关键类预测的有效性:一项实证研究
Shiyuan Zhou1, Wei Wu2, Jiale Wang2
1School of Information Engineering, Jiaxing Nanhu University, Jiaxing, China.
PloS one
|October 10, 2025
概括
本研究评估了软件工程中关键类预测 (KCP) 的网络指标. 结果显示,网络指标是有效的,特别是当与设计指标相结合时,指导改进KCP模型的功能选择.
科学领域:
- 软件工程 软件工程 软件工程
- 软件质量保证 软件质量保证
- 实证软件工程 实证软件工程
背景情况:
- 关键类对于理解软件系统至关重要,特别是对于新开发人员来说.
- 现有的关键类预测 (KCP) 模型主要侧重于项目内部预测,并使用未加权的网络指标.
- 以前的方法通常依赖于不完整的类合网络,限制了对网络指标有效性的评估,特别是在跨项目场景中.
研究的目的:
- 彻底评估用于关键类预测 (KCP) 的网络指标的有效性.
- 用设计指标,未加权网络指标和加权网络指标比较KCP模型的性能.
- 调查这些指标在项目内部和跨项目环境中的有效性.
主要方法:
- 构建加权和更完整的类合网络用于软件分析.
- 引入了一组加权网络指标来量化类复杂性.
- 开发和比较KCP模型使用随机森林和天真贝叶斯学习者与各种特征集 (设计指标,未加权/加权网络指标) 在项目内部和跨项目设置.
- 对18个开源Java项目进行实证研究.
主要成果:
- 网络指标,无论是未加权的还是加权的,都显示了KCP的显著有效性.
- 对KCP模型的最佳特征集因背景而异 (项目内部与跨项目).
- 在项目内部的KCP中,单独的网络指标或与设计指标一起的网络指标表现良好.
- 在跨项目KCP中,单独使用设计指标或与网络指标一起使用设计指标是有效的.
- 结合网络和设计指标在两种情况下都是有益的.
结论:
- 网络指标是构建有效关键类预测模型的有价值特征.
- 选择的特征 (设计指标与网络指标) 应根据特定的KCP环境量身定制.
- 这项研究为选择适当的指标提供了指导,以改善各种软件项目的KCP模型性能.
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