A Rationale-based Model for Architecture Design Reasoning

10.3. Reasoning about change impact with AREL
Figure 10.6: An Example BNN Shown with Beliefs Before Any Evidence is Entered
10.3.3 Predictive reasoning
As the requirement or the environment in the system changes, we want to predict their
effect in terms of possible architecture design changes. Being able to identify the areas
that are likely to change would improve both the accuracy and efficiency of architecture
enhancements. Recall from Section 10.2 that reasoning in a BBN means users first set
the values of certain nodes in the network to indicate evidence that has been gathered
about the system. For the AREL BBN model, setting values means that change takes
place in the system. These changes are then propagated through the network, producing
a new probability distribution over the remaining variables in the network [83], showing
the what-if scenarios of the impact of change. Multiple changes could be introduced
simultaneously. Let us consider how we can do this type of predictive reasoning in the
BBN representation of AREL.
To add one or more likely changes as evidence to the AREL model, an architect would
instantiate the BBN with evidence that an AE is “volatile”, i.e. volatile state is et to 100%.
The BBN belief updating algorithms would then compute the posterior probabilities for
AR and AE nodes which are affected by the additional evidence in the network. The
architect can assess the change impacts based on the posterior probabilities. In particular,
which decisions (AR nodes) are more likely to be invalid and which architecture elements
(AE nodes) are more likely to be volatile.
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