Parametric Bootstrapping for Assessing Software Reliability Measures

2011 IEEE 17th Pacific Rim International Symposium on Dependable Computing Pub Date : 2011-12-12 DOI:10.1109/PRDC.2011.10

Toshio Kaneishi, T. Dohi

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引用次数: 22

Abstract

The bootstrapping is a statistical technique to replicate the underlying data based on the resampling, and enables us to investigate the statistical properties. It is useful to estimate standard errors and confidence intervals for complex estimators of complex parameters of the probability distribution from a small number of data. In software reliability engineering, it is common to estimate software reliability measures from the fault data (fault-detection time data) and to focus on only the point estimation. However, it is difficult in general to carry out the interval estimation or to obtain the probability distributions of the associated estimators, without applying any approximate method. In this paper, we assume that the software fault-detection process in the system testing is described by a non-homogeneous Poisson process, and develop a comprehensive technique to study the probability distributions on significant software reliability measures. Based on the maximum likelihood estimation, we assess the probability distributions of estimators such as the initial number of software faults remaining in the software, software intensity function, mean value function and software reliability function, via parametric bootstrapping method.

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评估软件可靠性措施的参数引导

自举是一种基于重采样复制底层数据的统计技术，使我们能够研究统计特性。从少量数据中估计概率分布的复杂参数的复杂估计量的标准误差和置信区间是有用的。在软件可靠性工程中，通常是从故障数据(故障检测时间数据)中估计软件可靠性度量，并且只关注点估计。然而，一般来说，如果不使用任何近似方法，很难进行区间估计或获得相关估计量的概率分布。本文假设系统测试中的软件故障检测过程是用非齐次泊松过程来描述的，并发展了一种综合的方法来研究重要软件可靠性度量的概率分布。在极大似然估计的基础上，通过参数自举方法对软件中剩余的初始软件故障数、软件强度函数、均值函数和软件可靠性函数等估计量的概率分布进行了估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2011 IEEE 17th Pacific Rim International Symposium on Dependable Computing

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