Using Bayesian belief networks for change impact analysis in architecture design

Research into design rationale in the past has focused on argumentation-based design deliberations. These approaches cannot be used to support change impact analysis effectively because the dependency between design elements and decisions are not well represented and cannot be quantified. Without such knowledge, designers and architects cannot easily assess how changing requirements and design decisions may affect the system. In this article, we introduce the Architecture Rationale and Element Linkage (AREL) model to represent the causal relationships between architecture design elements and decisions. We apply Bayesian Belief Networks (BBN) to AREL, to capture the probabilistic causal relationships between design elements and decisions. We employ three different BBN-based reasoning methods to analyse design change impact: predictive reasoning, diagnostic reasoning and combined reasoning. We illustrate the application of the BBN modelling and change impact analysis methods by using a partial design of a real-world cheque image processing system. To support its implementation, we have developed a practical, integrated tool set for the architects to use. 2006 Elsevier Inc. All rights reserved.