A user‐assisted approach to component clustering

Abstract In this paper, we present a user‐assisted clustering technique for software architecture recovery based on a proximity measure that we call component association. The component association measure is computed on the shared properties among groups of highly related system entities. In this approach, the software system is modeled as an attributed relational graph with the software constructs (entities) represented as nodes and data/control dependencies represented as edges. The application of data mining techniques on the system graph allows us to generate a component graph where the edges are labeled by the association strength values among the components. An interactive partitioning technique is used to partition a system into cohesive components. Graph visualization tools and cluster quality evaluation metrics are applied by the user to assess and fine tune the partition result. Copyright © 2003 John Wiley & Sons, Ltd.