Comparing the decompositions produced by software clustering algorithms using similarity measurements

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Abstract
Decomposing source code components and relations into subsystem clusters is an active area of research. Numerous clustering approaches have been proposed in the reverse engineering literature, each one using a different algorithm to identify subsystems. Since different clustering techniques may not produce identical results when applied to the same system, mechanisms that can measure the extent of these differences are needed. Some work to measure the similarity between decompositions has been done, but this work considers the assignment of source code components to clusters as the only criterion for similarity. We argue that better similarity measurements can be designed if the relations between the components are considered. The authors propose two similarity measurements that overcome certain problems in existing measurements. We also provide some suggestions on how to identify and deal with source code components that tend to contribute to poor similarity results. We conclude by presenting experimental results, and by highlighting some of the benefits of our similarity measurements.

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