Software failure prediction based on a Markov Bayesian network model

Due to the complexity of software products and development processes, software reliability models need to possess the ability of dealing with multiple parameters. Also in order to adapt to the continually refreshed data, they should provide flexibility in model construction in terms of information updating. Existing software reliability models are not flexible in this context. The main reason for this is that there are many static assumptions associated with the models. Bayesian network is a powerful tool for solving this problem, as it exhibits strong ability to adapt in problems involving complex variant factors. In this paper, a software prediction model based on Markov Bayesian networks is developed, and a method to solve the network model is proposed. The use of our model is illustrated with an example.