Detecting approximate clones in business process model repositories.

Empirical evidence shows that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication arises for example when the repository covers multiple variants of the same processes or due to copy-pasting. Previous work has addressed the problem of efficiently retrieving exact clones that can be refactored into shared subprocess models. This paper studies the broader problem of approximate clone detection in process models. The paper proposes techniques for detecting clusters of approximate clones based on two well-known clustering algorithms: DBSCAN and Hierarchical Agglomerative Clustering (HAC). The paper also defines a measure of standardizability of an approximate clone cluster, meaning the potential benefit of replacing the approximate clones with a single standardized subprocess. Experiments show that both techniques, in conjunction with the proposed standardizability measure, accurately retrieve clusters of approximate clones that originate from copy-pasting followed by independent modifications to the copied fragments. Additional experiments show that both techniques produce clusters that match those produced by human subjects and that are perceived to be standardizable. [ABSTRACT FROM AUTHOR]