SCDetector: Software Functional Clone Detection Based on Semantic Tokens Analysis

Code clone detection is to find out code fragments with similar functionalities, which has been more and more important in software engineering. Many approaches have been proposed to detect code clones, in which token-based methods are the most scalable but cannot handle semantic clones because of the lack of consideration of program semantics. To address the issue, researchers conduct program analysis to distill the program semantics into a graph representation and detect clones by matching the graphs. However, such approaches suffer from low scalability since graph matching is typically time-consuming.

In this paper, we propose SCDetector to combine the scalability of token-based methods with the accuracy of graph-based methods for software functional clone detection. Given a function source code, we first extract the control flow graph by static analysis. Instead of using traditional heavyweight graph matching, we treat the graph as a social network and apply social-network-centrality analysis to dig out the centrality of each basic block. Then we assign the centrality to each token in a basic block and sum the centrality of