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ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Thu 24 Sep 2020 01:10 - 01:30 at Koala - Maintenance and Evolution (4) Chair(s): Xin Xia

Source code clone detection is to excavate code fragments with similar functionalities, which has been more and more important in software engineering. Many approaches have been proposed for detecting 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 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 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 the same token in different basic blocks. By this a graph is turned into certain tokens with graph semantics (i.e., centrality), called semantic tokens. In final, these semantic tokens are fed into a Siamese architecture neural network to train a model, and uses it to detect code clones. We evaluate SCDetector on two large datasets of functionally similar code. Experimental results indicate that our system is superior to state-of-the-art methods and the time cost of SCDetector is more than 14 times less than the state-of-the-art approach in detecting semantic clones.

Thu 24 Sep

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01:10 - 02:10
Maintenance and Evolution (4)Research Papers / Tool Demonstrations at Koala
Chair(s): Xin Xia Monash University
SCDetector: Software Functional Clone Detection Based on Semantic Tokens Analysis
Research Papers
Yueming Wu Huazhong University of Science and Technology, Deqing Zou Huazhong University of Science and Technology, Shihan Dou Huazhong University of Science and Technology, Siru Yang Huazhong University of Science and Technology, Wei Yang University of Texas at Dallas, USA, Feng Cheng Huazhong University of Science and Technology, Hong Liang Huazhong University of Science and Technology, Hai Jin Huazhong University of Science and Technology
Generating Concept based API Element Comparison Using a Knowledge Graph
Research Papers
Yang Liu Fudan University, China, Mingwei Liu Fudan University, China, Xin Peng Fudan University, China, Christoph Treude University of Adelaide, Australia, Zhenchang Xing Australian National University, Australia, Xiaoxin Zhang Fudan University, China
JITBot: An Explainable Just-In-Time Defect Prediction Bot
Tool Demonstrations
Chaiyakarn Khanan Mahidol University, Worawit Luewichana Mahidol University, Krissakorn Pruktharathikoon Mahidol University, Jirayus Jiarpakdee Monash University, Australia, Kla Tantithamthavorn Monash University, Australia, Morakot Choetkiertikul Mahidol University, Thailand, Chaiyong Rakhitwetsagul Mahidol University, Thailand, Thanwadee Sunetnanta Mahidol University