In this paper, we test a method for visualizing and measuring software portfolio architectures and use our measures to predict the costs of architectural change. Our data is drawn from a biopharmaceutical company, comprising 407 architectural components with 1,157 dependencies between them. We show that the architecture of this system can be classified as a “core-periphery” system, meaning it contains a single large dominant cluster of interconnected components (the “Core”) representing 32% of the system. We find that the classification of software applications within this architecture, as being either Core or Peripheral, is a significant predictor of the costs of architectural change. Using OLS regression models, we show that this measure has greater predictive power than prior measures of coupling used in the literature.
Visualizing and Measuring Software Portfolio Architectures: A Flexibility Analysis by Robert Lagerstrom, Carliss Y. Baldwin, Alan MacCormack, and David Dreyfus