Index-based model clone detection.

Existing algorithms for model clone detection operate in batch mode. Consequently, if a small part of a large model changes during maintenance, the entire detection needs to be recomputed to produce updated cloning information. Since this can take several hours, the lack of incremental detection algorithms hinders clone management, which requires up-to-date cloning information. In this paper we present an index-based algorithm for model clone detection that is incremental and distributable. We present a case study that demonstrates its capabilities, outline its current limitations and present directions for future work.