SHARP: a scalable approach to architecture-level reliability prediction of concurrent systems

  • Authors:

  • Leslie Cheung;Leana Golubchik;Nenad Medvidovic

  • Affiliations:

  • Univ of Southern California;Univ of Southern California;Univ of Southern California

  • Venue:
  • Proceedings of the 2010 ICSE Workshop on Quantitative Stochastic Models in the Verification and Design of Software Systems
  • Year:
  • 2010

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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.