Abstract

The software clustering literature contains many different approaches that attempt to automatically decompose software systems. These approaches commonly utilize criteria or measures based on principles such as high cohesion and low coupling, information hiding etc. In this paper, we present an algorithm that subscribes to a philosophy targeted towards program comprehension and based on subsystem patterns. We discuss the algorithm's implementation and describe experiments that demonstrate its usefulness. 1 Introduction A common approach that researchers in various disciplines use in order to deal with large data sets is to develop a taxonomy, i.e. create categories of objects that exhibit similar features or properties. Such categories (commonly referred to as clusters) can be discovered through a variety of techniques that have been proposed in the literature. Research on the effectiveness and behaviour of these techniques has given rise to the field of cluster analysis. Software ...

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