Discrete and Continuous Time High-Order Markov Models for Software Reliability Assessment

Due to the critical challenges and complexity of modern software systems developed over the last decade, there has arisen an ever increasing attention to look for products with high reliability at reasonable costs. Software development process moves toward component-based design, and architecture based approach in software reliability modeling is widely used. However, in lots of models for software reliability assessment the assumption of independent software runs is a simplification of real software behaviour. This paper describes two software reliability models that use high-order Markov chains thus taking into account dependencies among software component runs for more accurate software reliability assessment. The efficiency and accuracy of developed models is investigated by the example of several software products. It is shown that using the software reliability models based on the high-order Markov chains results in the software reliability assessment accuracy up to 10–20%.