A Rationale-based Model for Architecture Design Reasoning

Chapter 10
Architecture decision dependency
and causality
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. In earlier chapters, we have described change impact analysis
using qualitative and traceability methods. In this chapter, we extend the AREL model
to represent quantifiable 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. These methods support the prediction and
diagnosis of change impacts in the architecture design in a quantitative manner [83].
1
We illustrate the application of the BBN modelling and change impact analysis methods
by using the case study illustrated in Chapter 7.
10.1 Background
As discussed in early chapters, design rationale has many benefits and it can be used
to verify and trace the design of a system as well as to support its enhancements. The
approach outlined in this chapter is to represent quantitatively the sensitivity to changes
1 This chapter is based on our work published in [162, 163]. Nicholson has contributed to applying BBN
in AREL as well as the writing in the papers. Han and Jin have contributed to the development of the
papers.
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