OSS的可靠性评估:在运行阶段使用变更点的方法

N. Gandhi, Neha, A. Aggarwal, Abhishek Tandon
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引用次数: 4

摘要

自20世纪70年代以来,在非齐次泊松过程(NHPP)的基础上提出的软件可靠性增长模型(SRGMs)是分析软件可靠性增长的最普遍的方法。此后,文献见证了srgm在不同假设、参数、性能等方面的演变。本研究试图为开源软件(OSS)的可靠性增长现象建模。由于OSS所遵循的开发过程与专有软件有很大的不同,传统的软件工程SDLC模型无法对其进行开发。OSS开发过程没有包含一个专门的测试阶段,因此在软件发布之前的测试上花费的精力可以忽略不计。因此,其业务阶段志愿人员参与的数量成为反映其可靠性增长的一个重要标准。此外,由于故障密度的变化、不规则的团队规模、志愿者专业知识等原因,故障检测过程(变更点)中的突然变化的影响也被捕获在建议的SRGM中。为了更好地适应软件故障过程的异构性,提出了一种基于用户增长的可靠性模型,并引入了变更点的概念。本文采用GNOME 2.0的失效数据进行可靠性增长分析。利用最小二乘估计回归技术对未知参数进行估计。采用均方误差(MSE)、决定系数(R2)、预测比率风险(PRR)和预测能力(PP)等拟合优度标准对所提出的SRGM进行性能检验。研究认为,在SRGM中引入变化点提高了模型的预测能力,从而为其适用性提供了证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating reliability for OSS: An approach with change-point in operational phase
Software reliability growth models (SRGMs) proposed on the foundations of Non-Homogenous Poisson Process (NHPP) have been the most accepted way to analyze reliability growth of a software since 1970's. Henceforth, Literature witnesses the evolution of SRGMs in terms of different assumptions, parameters, performance etc. This study is an attempt to model reliability growth phenomenon for Open Source Software (OSS). As the development process followed by OSS significantly differs from proprietary software, traditional software engineering SDLC models can't be followed for its development. OSS development procedure does not incorporate a dedicated testing phase and therefore negligible effort expenditure is done on testing of software prior its release. The volume of volunteer participation in its operational phase thus becomes a significant criterion in the representation of its reliability growth. Also, the impact of sudden changes in fault detection process (change-point) due to several reasons like variations in fault density, irregular team size, volunteer expertise etc is captured in proposed SRGM. This paper suggests a reliability model based on user growth with the concept of change point to better cater the heterogeneity in software failure process. Failure data of GNOME 2.0 is used in this paper reliability growth analysis. Unknown parameters are estimated using Least Square Estimation regression technique. Performance inspection of proposed SRGM is carried out using goodness-of-fit criteria like Mean square error (MSE), Coefficient of Determination (R2), Predictive Ratio Risk (PRR), and Predictive Power (PP). The study recommends that introduction of the change-point in SRGM improves prediction capability of the model and hence establishes an evidence of its applicability.
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