Long-lived systems will experience many successive changes during their lifecycle as they are adapted to meet new system requirements. Existing change propagation tools predict how changes to a system’s design at a fixed point in its life are likely to spread, but have not been extended to consider a series of successive modifications where the change probabilities update. This change in propagation probabilities in response to successive changes is introduced as Dynamic Change Propagation (DCP). This paper integrates research from change propagation, network theory, and excess to achieve the following objectives: 1) describe how a DCP model predicts system propagation change trajectories, 2) use a new synthetic test case generator to correlate network parameters like degree distribution with DCP, and 3) determine the correlations between a measure of DCP and a selection of existing change propagation metrics. Results indicate that DCP is limited by reducing the number of dependencies between components (affirming the usefulness of adding modularity to a system) and including high degree component ‘hubs’ between components.

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