Design & analysis of fault tolerant digital systems
Design & analysis of fault tolerant digital systems
Coverage Modeling for Dependability Analysis of Fault-Tolerant Systems
IEEE Transactions on Computers
Fault Injection for Dependability Validation: A Methodology and Some Applications
IEEE Transactions on Software Engineering
Fault simulation using small fault samples
Journal of Electronic Testing: Theory and Applications
Estimators for Fault Tolerance Coverage Evaluation
IEEE Transactions on Computers - Special issue on fault-tolerant computing
System Dependability Evaluation via a Fault List Generation Algorithm
IEEE Transactions on Computers
Coverage Estimation Methods for Stratified Fault-Injection
IEEE Transactions on Computers
Stress-Based and Path-Based Fault Injection
IEEE Transactions on Computers
Dependability: Basic Concepts and Terminology
Dependability: Basic Concepts and Terminology
Reliability modeling techniques for self-repairing computer systems
ACM '69 Proceedings of the 1969 24th national conference
FTCS '95 Proceedings of the Twenty-Fifth International Symposium on Fault-Tolerant Computing
A software integration approach for designing and assessing dependable embedded systems
Journal of Systems and Software
| Hi-index | 14.98 |
The existing classes of fault coverage models require an a priori distribution for collected data in their analysis. Using these models, analyses can be performed using various assumed distributions. The assumed distributions may not accurately reflect the behavior of the collected data and, as a result, the coverage values predicted by the models may be inaccurate, especially if testing yields little or no failure data. Since the occurrence of an uncovered fault in an ultra-dependable system is a rare event, then statistics of the extremes can be used to quantify uncoverage estimates in such systems. Statistics of the extremes provides for an analysis of rare event data without requiring any a priori knowledge of its distribution. It classifies most distributions into one of three asymptotic families; that is, in the limit, most distributions converge to one of three forms. Using statistics of the extremes, a coverage model is developed for when testing reveals no failures. From this model, the number of fault injection experiments required to demonstrate that a desired coverage level can be met is derived, as is the probability that this coverage estimate can be met.