A Bayesian model for predicting reliability of software systems at the architectural level

  • Authors:

  • Roshanak Roshandel;Nenad Medvidovic;Leana Golubchik

  • Affiliations:

  • Computer Science & Software Engineering Department, Seattle University, Seattle, WA;Computer Science Department, University of Southern California, Los Angeles, CA;Computer Science Department, University of Southern California, Los Angeles, CA and EE Systems Department, IMSC, University of Southern California, Los Angeles, CA

  • Venue:
  • QoSA'07 Proceedings of the Quality of software architectures 3rd international conference on Software architectures, components, and applications
  • Year:
  • 2007

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Abstract

Modern society relies heavily on complex software systems for everyday activities. Dependability of these systems thus has become a critical feature that determines which products are going to be successfully and widely adopted. In this paper, we present an approach to modeling reliability of software systems at the architectural level. Dynamic Bayesian Networks are used to build a stochastic reliability model that relies on standard models of software architecture, and does not require implementation-level artifacts. Reliability values obtained via this approach can aid the architect in evaluating design alternatives. The approach is evaluated using sensitivity and uncertainty analysis.