Evaluating software clustering using multiple simulated authoritative decompositions

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 LimSim, a novel approach for software clustering evaluation that utilizes multiple simulated authoritative decompositions. We also present experimental results of applying the new approach to evaluate various software clustering algorithms. The results demonstrate the usefulness of LimSim.