FERRARI: A Flexible Software-Based Fault and Error Injection System
IEEE Transactions on Computers - Special issue on fault-tolerant computing
Estimators for Fault Tolerance Coverage Evaluation
IEEE Transactions on Computers - Special issue on fault-tolerant computing
Fault Injection Techniques and Tools
Computer
Fault Injection and Dependability Evaluation of Fault-Tolerant Systems
IEEE Transactions on Computers
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Coverage estimation using statistic of the extremes for when testing reveals no failures: 3
IEEE Transactions on Computers
Coverage Estimation Using Statistics of the Extremes for When Testing Reveals No Failures
IEEE Transactions on Computers
Experimental Evaluation of the Unavailability Induced by a Group Membership Protocol
EDCC-4 Proceedings of the 4th European Dependable Computing Conference on Dependable Computing
Experimental Evaluation of Fault Handling Mechanisms
SAFECOMP '01 Proceedings of the 20th International Conference on Computer Safety, Reliability and Security
An Approach for Analysing the Propagation of Data Errors in Software
DSN '01 Proceedings of the 2001 International Conference on Dependable Systems and Networks (formerly: FTCS)
EPIC: Profiling the Propagation and Effect of Data Errors in Software
IEEE Transactions on Computers
An approach to experimentally obtain service dependability characteristics of the Jgroup/ARM system
EDCC'05 Proceedings of the 5th European conference on Dependable Computing
Dependable composite web services with components upgraded online
Architecting Dependable Systems III
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This paper addresses the problem of estimating fault tolerance coverage through statistical processing of observations collected in fault-injection experiments. In an earlier paper, various 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驴a vectorial statistic驴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.