Coverage estimation methods for stratified fault-injection

This paper addresses the problem of estimating fault tolerance coverage through statistical processing of observations collected in fault-injection experiments. In an earlier paper, venous estimators based on simple sampling in the complete fault/activity input space and stratified sampling in a par...

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Bibliographic Details
Published in:IEEE transactions on computers 1999-07, Vol.48 (7), p.707-723
Main Authors: Cukier, M., Powell, D., Ariat, J.
Format: Article
Language:English
Subjects:
Applied sciences
Approximation
Bayesian methods
Confidence
Confidence intervals
Confidence limits
Electronics
Estimates
Exact sciences and technology
Fault tolerance
Fault tolerant systems
Hardware
Life estimation
Mathematical analysis
Parameter estimation
Reliability
Sampling
Sampling methods
Statistical distributions
Statistics
System recovery
Testing
Virtual prototyping
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Summary:This paper addresses the problem of estimating fault tolerance coverage through statistical processing of observations collected in fault-injection experiments. In an earlier paper, venous estimators based on simple sampling in the complete fault/activity input space and stratified sampling in a partitioned space were studied; frequentist confidence limits were derived based on a normal approximation. In this paper, the validity of this approximation is analyzed. The theory of confidence regions is introduced to estimate coverage without approximation when stratification is used. Three statistics are considered for defining confidence regions. It is shown that one-a vectorial statistic-is often more conservative than the other two. However, only the vectorial statistic is computationally tractable. We then consider Bayesian estimation methods for stratified sampling. Two methods are presented to obtain an approximation of the posterior distribution of the coverage by calculating its moments. The moments are then used to identify the type of the distribution in the Pearson distribution system, to estimate its parameters, and to obtain the coverage confidence limit. Three hypothetical example systems are used to compare the validity and the conservatism of the frequentist and Bayesian estimations.
ISSN:0018-9340
1557-9956
DOI:10.1109/12.780878