In the software landscape, understanding component impacts on system reliability is pivotal, especially given the unique complexities of modern software systems. This paper presents a model tailored for software reliability assessment. Our approach introduces the 'component influence' to measure a single component's effect on overall system reliability. Additionally, we adapt a state transition model to cater to the diverse architectures of software systems. Using a discrete-time Markov chain, we predict software reliability. We tested our model on an actual software system, finding it notably accurate and superior to existing methods. Our work offers a promising direction for those venturing into software reliability enhancement.
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Alyahyan et al. (2024) studied this question.
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