Reliability Growth of Fault - Tolerant Software

Two fault-tolerant software techniques are investigated: recovery block and N-version programming. For each, the stable reliability model is transformed into a model that considers reliability growth via the transformation approach based on the hyperexponential model. Analytic and numeric processing of the transformed models identify the influence of fault removal on the reliability of the fault-tolerant software approaches. The modeling approach is based on the transformation of a Markov chain of the fault-tolerant software system in stable reliability into another, modified Markov chain which enables reliability growth to be considered. This approach has allowed reliability growth relative to the classes of faults (independent, related) affecting fault-tolerant software to be identifed and evaluated. The evaluations apply to systems of short successive mission durations with respect to the system life-time. Using generalized stochastic Petri nets to model the fault-tolerant software systems allows for an automatic application of the transformation technique. Analytic expressions are derived only to analyze explicitly the impact of fault-removal of each class. In practice, reliability measures can be directly evaluated by available tools for numerical processing of the Markov chains. Even though this work is a f is t attempt, the results are important since they show the influence of reliability growth on the reliability of fault-tolerant software systems. These results: a) confirm, from the reliability growth perspective, the importance of the faults whose occurrence can lead to common-mode failures, eg, decider faults and re2ded faults and b) enable the impact of these faults to be quantified.