Quantitative Trade-off Analysis in Quality Attributes of a Software Architecture Using Bayesian Network Model

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. We apply Bayesian Network Model (BNM), to capture the probabilistic causal relationships between design elements and decisions. We employ three different BNMbased reasoning methods to analyze the trade-off between the conflicting quality attributes. Markov blanket discovery algorithms can be used for quality assessment BNMs. Additionally, work will be done to determine how known optimization methods such as Tabu search may be applied in the context of the proposed framework. Ultimately, the goal is to create a possibility of automatic execution of steps involved in architectural optimization.