考虑硬件相关软件故障的嵌入式软件可靠性模型

Jinhee Park, Hyeon-Jeong Kim, Ju-Hwan Shin, Jongmoon Baik
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引用次数: 25

摘要

由于嵌入式系统中的软件负责控制软件和硬件组件,因此对这些软件进行更准确的可靠性评估的重要性已经增加。为了估计目标软件系统的可靠性,经常使用软件可靠性模型和软件故障数据。由于软件和硬件在嵌入式系统中高度相关并且经常相互交互,因此两者都是导致软件故障的因素。因此,在评估软件可靠性时,应考虑软硬件故障对软件故障的影响。然而,许多研究人员建立的软件可靠性模型假设软件故障仅由软件故障引起,这可能导致可靠性估计不准确。在本文中,我们提出了两种新的可靠性模型,将软件故障和硬件故障作为嵌入式软件可靠性评估的根本原因。将所建模型与现有模型进行了有效性比较,并给出了模型与实际工程数据的分析结果。实验结果表明,考虑硬件退化特征的Weibull模型具有较高的拟合充分性和较好的软件可靠性估计精度。通过这些结果,该模型提供了更准确的软件可靠性评估,并有助于在嵌入式软件测试的早期阶段制定更好的测试策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Embedded Software Reliability Model with Consideration of Hardware Related Software Failures
As software in an embedded system has taken charge of controlling both software and hardware components, the importance of estimating more accurate reliability for such software has been increased. To estimate the reliability of target software systems, software reliability models are often utilized with software failure data. Since software and hardware are highly co-related and frequently interact with each other in embedded systems, both of them are contributing factors to software failures. Thus, the influence of software and hardware faults on software failures should be taken account for to estimate software reliability. However, many researchers have developed software reliability models assuming that software failures are caused by only software faults, which might lead to inaccurate reliability estimation. In this paper, we suggest two new reliability models considering software and hardware faults as root causes of software failures for embedded software reliability estimation. The proposed models are compared with existing models for validity, and analysis results of the models with real project data are presented. The experimental results show that a Weibull based model, which takes characteristics of hardware degradation into account, has higher fitting-adequacy and superior accuracy for software reliability estimation. Through these results, the proposed model provides more accurate software reliability estimation and helps setting better testing strategies in the earlier phases of the embedded software testing.
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