TY - JOUR T1 - Coverage estimation methods for stratified fault-injection JF - Computers, IEEE Transactions on Y1 - 1999 A1 - Michel Cukier A1 - Powell,D. A1 - Ariat,J. KW - Bayes methods KW - Bayesian estimations KW - confidence regions KW - coverage estimation methods KW - fault tolerance coverage KW - fault tolerant computing KW - frequentist confidence limits KW - parameter estimation KW - parameters estimation KW - Pearson distribution system KW - statistical processing KW - stratified fault-injection KW - stratified sampling KW - vectorial statistic AB - 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 VL - 48 SN - 0018-9340 CP - 7 M3 - 10.1109/12.780878 ER -