Predicting Change Propagation on Different Levels of Granularity: An Algorithmic View

Abstract

When using connectivity models to assess the potential impacts of component changes on other parts of a product, plausible inferences are readily assessed when such products are represented at the appropriate level of granularity to support specific queries. In this paper, we describe the development of a prediction algorithm, which enables coherent computations of the likelihoods of change propagating on several levels of detail of product description given component level change probabilities. The results show that a multilevel approach to change prediction supports an increased range of design queries beyond that achievable with a single level model. Such change prediction capability is useful when carrying out a comprehensive change impact assessment.

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