Frequentist and Bayesian Coverage Estimations for Stratified Fault-Injection

This paper addresses the problem of estimating the coverage of fault tolerancethrough statistical processing of observations collected in fault-injection experiments. In
an earlier paper, we have studied various frequentist estimation methods based on simple
sampling in the whole fault/activity input space and stratified sampling in a partitioned
space. In this paper, Bayesian estimation methods are introduced 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. Two hypothetical example systems are used to compare the
validity and the conservatism of the Bayesian and frequentist estimations. It is shown that
one Bayesian estimation method is valid for both examples and that its estimations are
much less conservative than the frequentist ones. However, the Bayesian estimations for
stratified sampling are still conservative compared to estimations for simple sampling.