Bayesian recurrent model of reliability growth: beta-uniform distribution of parameters

Forecasting reliability of complex modifiable information systems is one of the topical problems of the mass service theory nowadays. Any first established complex system designed for processing or transmission of information flows, as a rule, does not possess the required reliability. Such systems are subject to modifications during development, testing, and regular functioning. The purpose of such modifications is to increase reliability of information systems. In this connection, there is a necessity to formalize the concept of reliability of modifiable information systems and to develop methods and algorithms of estimation and forecasting of various reliability characteristics. One approach to determine system reliability is to compute the probability that the signal fed to the input of the system at a given point of time will be reacted to correctly by the system. The article considers the exponential recurrent growth model of reliability, in which the probability of system reliability is represented as a linear combination of “defectiveness” and “efficiency” parameters of tools correcting the deficiencies in the system. It is assumed that the researcher does not have exact information about the system under study and is only familiar with the characteristics of the class from which this system is taken. In the framework of the Bayesian approach, it is assumed that one of the indicators of “defectiveness” and “efficiency” has the beta-distribution and the other one has the uniform distribution. Average marginal system reliability is calculated. Numerical results for model examples are obtained.