A Rationale-based Model for Architecture Design Reasoning

10.3. Reasoning about change impact with AREL
Figure 10.7: Predictive Model
Let us assume that the bank wants to enforce a stronger privacy policy. It means that
the requirement R2 4 3 would change and become volatile. This change is inserted as
evidence to set the volatile state of R2 4 3 to 100% (see highlighted node in Figure 10.7).
Given this evidence, we can do a what-if analysis and predict that there is a 90% chance
that the decision AR29 will be invalid leading to a 82% chance that design decision C4 2 17
will be volatile. This prediction is based on the strength of the relationship, represented
by CPT, between architecture design reasoning and its motivational reason, as well as the
strength of the relationship between the design outcomes and the design reasoning. We
predict that if R2 4 3 is to change, there is a 82% chance that C4 2 17 would be affected
and subject to change.
This volatility also ripples through AR16, C4 2 9, AR13, C4 2 6, AR30 and C4 2 18.
This is because all these nodes are indirectly and conditionally dependent on R2 4 3. For
the ARs, the probabilities of their validity have decreased. For the AEs, the probabilities
of their volatility have increased. Note that apart from these nodes, the rest of the BBN
network remain unchanged. This is because those nodes are conditionally independent of
R2 4 3 and its descendants. That is, a change in R2 4 3 has no effects on them. This
phenomenon is called direction-dependent separation or simply d-separation (see [73, 83]
for details).
BBN enables architects to predict the likelihood of change impact in the related parts
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