测量驱动的仪表板为大型系统的需求和设计提供了领先的指标

R. Selby
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引用次数: 14

摘要

度量驱动的指示板为理解、评估和预测大规模系统和过程的开发、管理和经济提供了统一的机制。仪表板支持复杂信息的交互式图形显示,并支持灵活的分析功能,以实现用户可定制性和可扩展性。仪表板通常包括软件需求和设计度量,因为它们提供了项目规模、增长和稳定性的主要指标。本文的重点是在实际大型项目中使用的仪表板,以及仪表板揭示的实例经验关系。实证结果集中在大型系统需求和设计的领先指标上。在第一组关注需求度量的14个项目中,软件需求与源代码行数的比例平均为1:46。远远超过1:46需求与代码比例的项目在验证过程中往往更加费力且容易出错。在第二组关注设计度量的16个项目中,在按大小归一化后,组件内部状态数量的前四分位数的组件平均比组件内部状态数量的后四分位数的组件多6.2倍。在按大小归一化后,组件交互数量的前四分位数的组件平均比底部四分位数的组件多4.3倍。当组件内部状态的数量处于下四分位数时,即使组件交互的数量处于上四分位数,无论大小归一化,组件的故障倾向也较低。测量驱动的仪表板揭示了增加大规模系统可见性的洞察力,并向组织和项目提供反馈
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measurement-driven dashboards enable leading indicators for requirements and design of large-scale systems
Measurement-driven dashboards provide a unifying mechanism for understanding, evaluating, and predicting the development, management, and economics of large-scale systems and processes. Dashboards enable interactive graphical displays of complex information and support flexible analytic capabilities for user customizability and extensibility. Dashboards commonly include software requirements and design metrics because they provide leading indicators for project size, growth, and stability. This paper focuses on dashboards that have been used on actual large-scale projects as well as example empirical relationships revealed by the dashboards. The empirical results focus on leading indicators for requirements and design of large-scale systems. In the first set of 14 projects focusing on requirements metrics, the ratio of software requirements to source-lines-of code averaged 1:46. Projects that far exceeded the 1:46 requirements-to-code ratio tended to be more effort-intensive and fault-prone during verification. In the second set of 16 projects focusing on design metrics, the components in the top quartile of the number of component internal states had 6.2 times more faults on average than did the components in the bottom quartile, after normalization by size. The components in the top quartile of the number of component interactions had 4.3 times more faults on average than did the components in the bottom quartile, after normalization by size. When the number of component internal states was in the bottom quartile, the component fault-proneness was low even when the number of component interactions was in the upper quartiles, regardless of size normalization. Measurement-driven dashboards reveal insights that increase visibility into large-scale systems and provide feedback to organizations and projects
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