A Bayesian Model for Predicting Reliability of Software Systems at the Architectural Level

Modern society relies heavily on complex software systems for everyday activities. Dependability of these systems thus has become a critical feature that determines which products are going to be successfully and widely adopted. In this paper, we present an approach to modeling reliability of software systems at the architectural level. Dynamic Bayesian Networks are used to build a stochastic reliability model that relies on standard models of software architecture, and does not require implementation-level artifacts. Reliability values obtained via this approach can aid the architect in evaluating design alternatives. The approach is evaluated using sensitivity and uncertainty analysis.