Abstract
The detection of model clone has been an active research area in recent years. The closed clone instances contain all the information of model clones so they can ensure the completeness of detection results essentially. In order to improve the degree of completeness in clone detection, a novel model clone detection algorithm named CL_MCD (Closed Model Clone Detection) is proposed. CL_MCD focuses on exactly matched clones and aims to find all the closed clone instances. The main innovation of CL_MCD is in the detection phase. Every time after finding a new node pair with the same label in the breadth-first search of model graph, CL_MCD transforms all the node pairs into a clone pair, and puts the clone pair into a set that contains all the candidate clone instances if its size is greater than or equal to the size of minimum clone. Then every candidate clone instance is compared with all the others in the set. If a candidate clone instance is one part of any other instance, it is deleted. After the filtering, redundant clone instances are removed and only the closed clone instances are kept in the set. Theoretical analysis and experimental studies demonstrate that CL_MCD has higher degree of completeness than CloneDetective.
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