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- W2034267675 abstract "Early prediction of reliability is important in building dependable software. Existing approaches are unable to model concurrent systems in a scalable way. To address the scalability challenge, we propose a framework that is applicable at the architecture level. Our framework achieves scalability by approaching the system from the perspective of usage scenarios and by employing a hierarchical solution. Specifically, we solve lower granularity scenario-based submodels and a higher granularity system model; we then combine their results to obtain a system reliability estimate. Our evaluation indicates that (a) the proposed hierarchical framework is accurate, and (b) that it is more scalable than existing techniques." @default.
- W2034267675 created "2016-06-24" @default.
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- W2034267675 date "2010-05-03" @default.
- W2034267675 modified "2023-09-27" @default.
- W2034267675 title "SHARP" @default.
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- W2034267675 doi "https://doi.org/10.1145/1808877.1808879" @default.
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