On the Comparability of Software Clustering Algorithms

Abstract

Evaluation of software clustering algorithms is typically done by comparing the clustering results to an authoritative decomposition prepared manually by a system expert. A well-known drawback of this approach is the fact that there are many, equally valid ways to decompose a software system, since different clustering objectives create different decompositions. Evaluating all clustering algorithms against a single authoritative decomposition can lead to biased results. In this paper, we introduce and quantify the notion of clustering algorithm comparability. It is based on the concept that algorithms with different objectives should not be directly compared. Not surprisingly, we find that several of the published algorithms in the literature are not comparable to each other.