Software clustering based on information loss minimization
Periklis Andritsos, Vassilios Tzerpos · 2004
The majority of the algorithms in the software cluster-ing literature utilize structural information in order to decom-pose large software systems. Other approaches, such as using £le names or ownership information, have also demonstrated merit. However, there is no intuitive way to combine informa-tion obtained from these two different types of techniques. In this paper, we present an approach that combines struc-tural and non-structural information in an integrated fashion. LIMBO is a scalable hierarchical clustering algorithm based on the minimization of information loss when clustering a software system. We apply LIMBO to two large software systems in a num-ber of experiments. The results indicate that this approach produces valid and useful clusterings of large software sys-tems. LIMBO can also be used to evaluate the usefulness of various types of non-structural information to the software clustering process. 1