选择最佳的软件代码描述符-Java的案例
Yegor Bugayenko1, Zamira Kholmatova2, Artem Kruglov2
1Huawei, Moscow, Russia.
PloS one
|November 1, 2024
概括
选择正确的软件指标对于项目成功至关重要. 本研究使用先进的算法识别了软件工程指标的最佳子集,显著降低了Java程序的复杂性并改善了对Java程序的项目管理.
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:
- 软件指标和工具的扩散在选择和管理方面带来了挑战.
- 现有的指标可能会受到诸如对线性和过拟合等理论问题的影响.
- 实际管理众多指标是困难的,特别是对于较小的公司.
研究的目的:
- 为有效的软件项目和产品管理确定可行的软件指标子集.
- 解决由大量软件指标所带来的理论和实际挑战.
- 开发和验证一种选择Java程序最佳度量子集的方法.
主要方法:
- 利用粒子优化和遗传算法来识别最佳度量子集.
- 专注于Java程序,在类和方法层面分析指标.
- 采用萨蒙误差作为衡量度量相似性的衡量标准.
主要成果:
- 在800个GitHub项目中进行了实验,并在200个项目中进行了验证.
- 实现了软件工程指标的最佳子集.
- 在验证数据集上,在类和方法级别显示了较低的萨蒙误差值 (>70%).
结论:
- 拟议的方法有效地识别了软件指标的最佳子集.
- 这些子集显著降低了软件项目的复杂性,并提高了软件项目的可管理性.
- 该方法为选择项目和产品管理至关重要的指标提供了可行的解决方案.
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