题名

Weighted Least-Square Esimate for Software Error Intensity

并列篇名

軟體錯誤強度之加權最小平方估計

DOI

10.29977/JCIIE.200803.0007

作者

葛壽農(Show-Long Patrick Koh)

关键词

軟體錯誤強度 ； 非均勻波氏模型 ； 加權最小平方分估計 ； 拉普拉斯轉換 ； SRGM ； software error intensity ； non-homogenous Poisson process ； weighted least-square estimates ； Laplace transform ； SRGM

期刊名称

工業工程學刊

卷期/出版年月

25卷2期（2008 / 03 / 01）

页次

162 - 173

内容语文

英文
中文摘要

對於參數模型軟體錯誤強度，本文首次建議加權最小平方(WLS)估計。該估計與廣被喜好的最大可能(ML)估計只在特定條件下存在的狀況不同，只要得知錯誤數的期望值與期望變異數，便總是存在。在該估計中，期望變異數的倒數是估計誤差的權，據此，期望變異數應是測試時間的降函數，方能滿足賦予較新錯誤較重之權的直觀。這個條件大致可被RK模型滿足，但卻無法被最流行的非均勻波氏模型滿足。本文提出WLS估計的理論推導，根據一組真實數據，進行數值上的測試，並與LS估計進行比較。對於其它種類例如在錯誤間距上作假設的參數模型，該加權概念亦被簡短地討論。
英文摘要

For estimating the software error intensity of parametric models, weighted least-square (WLS) estimate is suggested for the first time. Unlike the widely-favored maximum likelihood (ML) estimate which only exists under certain conditions, the WLS estimate always exists as long as the expected mean and the expected variance of error count are available. The weight to the square of estimating error is chosen to be the reciprocal of the expected variance which should decrease with testing time in order to meet the intuition of weighing heavy on recently detected errors. This requirement is essentially met by the error count of RK model, but not by the most popular non-homogenous Poisson model. The WLS estimate is theoretically developed and numerically compared with least-square estimate on a set of real data. The idea of weighted estimation is also briefly discussed for other parametric models which make assumptions on the time elapsed between successive errors.
主题分类 工程學 > 工程學總論
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