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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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基于基于组件的通用化G-O模型的软件系统可靠性的全面评估.

Yuzhuo Wang1,2, Haitao Liu2, Haojie Yuan2

  • 1College of Weaponry Engineering, Naval University of Engineering, Wuhan, Hubei, China.

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概括

本研究介绍了一种基于组件的通用化G-O模型 (CB-GGOM),用于预测软件可靠性增长. 该模型可以在没有集成测试数据的情况下进行早期可靠性预测,帮助预防缺陷和优化测试策略.

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基于组件的软件是基于组件的软件.检测故障的速度 检测故障的速度不同质的 波桑过程 不同质的 波桑过程剩余故障的数量.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 可靠性工程可靠性工程
  • 计算机科学 计算机科学

背景情况:

  • 在早期开发中预测软件可靠性增长对于减少浪费至关重要,但由于数据有限,具有挑战性.
  • 现有模型通常需要大量的集成测试数据,这限制了它们在早期设计和集成阶段的适用性.

研究的目的:

  • 为基于组件的软件系统开发一种新的可靠性增长模型,适用于早期开发阶段.
  • 为了使准确的可靠性预测只使用组件级可靠性数据,而不依赖系统集成测试数据.

主要方法:

  • 定义了两个系统级参数:总系统故障和系统故障检测率.
  • 在组件和系统故障检测率和总故障之间建立了关系.
  • 构建了基于组件的通用G-O模型 (CB-GGOM) 和两个早期和稳定的集成测试阶段的近似模型.

主要成果:

  • 从已知的组件参数成功计算了系统参数.
  • 开发了CB-GGOM及其近似值,通过模拟和现实世界的例子进行了验证.
  • 证明了模型在没有集成测试数据的情况下预测可靠性增长的有效性.

结论:

  • 拟议的CB-GGOM及其近似方法为早期软件可靠性预测提供了一种可行的方法.
  • 这些模型使开发人员能够优化测试策略并实施主动缺陷预防.
  • 该方法克服了在软件开发的设计和集成阶段通常遇到的数据限制.