An empirical study of stochastic differential equation models based on component importance level for open source software

Network technologies become increasingly more complex in a wide sphere. Especially, open source software systems which serve as key components of critical infrastructures in the society are still ever-expanding now. In this paper, we propose a new approach to software reliability assessment by creating a fusion of neural network and stochastic differential equations based on component importance levels. Also, we analyze actual software fault-count data to show numerical examples of software reliability assessment considering component importance levels for an open source software. Moreover, we compare the goodness-of-fit of the proposed models with the conventional software reliability growth model for OSS.