| 摘要: |
| 数量激增的软件系统被开发出来为用户提供了极大的便利，但也给系统开发带来了极大的不确定性.故障调试过程中的故障检测率（fault detection rate，简称FDR）是不规律变化的，且通常被描述为白色噪音.白色噪音具有马尔可夫性，但在实践中，噪音出现非马尔可夫性是普通现象，而噪音呈现马尔可夫性仅仅是例外.在许多真实情况下，白色噪声的理想化假设是不足的：真正的不规则因素总是非马尔可夫相关性的.提出了一个新的模型来量化调试过程相关的环境不确定性因素.基于广泛应用于软件故障检测过程的非齐次泊松过程（non-homogeneous Poissonprocess，简称NHPP）模型，将环境的不确定性考虑为任意分布和时间相关性的噪声.通过与一些现有模型的比较，新的框架表现出更接近实际观测数据的特征.除了常用的关注故障数的平均值以外，还提供了公式以计算其累积密度函数（CDF）和概率密度函数（PDF），从而获得调试过程的完整统计信息. |
| 关键词: 可靠性 不确定性 NHPP 噪音 相关性 |
| DOI：10.13328/j.cnki.jos.005108 |
| 分类号: |
| 基金项目:航空科学基金（2013ZC51023） |
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| Characterizing Uncertainty of Software Reliability Growth Model |
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XU Jia-Jun, YAO Shu-Zhen
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School of Computer Science and Engineering, Beihang University, Beijing 100191, China
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| Abstract: |
| More and more software systems have been developed to provide great flexibility to customers, but they also introduce great uncertainty to software development. The fault detection rate (FDR) within the fault detection process shows an irregular fluctuation and is usually modeled as a white noise. White noise is Markovian, but Non-Markov is the rule while Markov is the exception. In many cases the white noise idealization is insufficient, as real fluctuations are always correlated noise (non-Markovian noise). This study proposes a novel model to quantify the uncertainties associated with the debugging process. Based on the Non-homogeneous Poisson process (NHPP) model for software fault detection process, the environmental uncertainties are considered collectively as a noise of arbitrary distribution and correlation structure. Through a number of comparisons with existing methods, the new model exhibits a closer fitting to observation data. In addition to focusing on the mean value of detected-fault number, this work provides a formula to compute its cumulative density function (CDF) and probabilistic density function (PDF), thus encapsulating full statistical information of the debugging process. |
| Key words: reliability uncertainty NHPP noise correlation |
