2021/11/28 by Cheng Zhi Huang, Huang, Cheng, Hui Zhou +5
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software Engineering Research #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2111.14183
openalex publication_date 2021/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The code clone detection method based on semantic similarity has important value in software engineering tasks (e.g., software evolution, software reuse). Traditional code clone detection technologies pay more attention to the similarity of code at the syntax level, and less attention to the semantic similarity of the code. As a result, candidate codes similar in semantics are ignored. To address this issue, we propose a code clone detection method based on semantic similarity. By treating code as a series of interdependent events that occur continuously, we design a model namely EDAM to encode code semantic information based on event embedding and event dependency. The EDAM model uses the event embedding method to model the execution characteristics of program statements and the data dependence information between all statements. In this way, we can embed the program semantic information into a vector and use the vector to detect codes similar in semantics. Experimental results show that the performance of our EDAM model is superior to state of-the-art open source models for code clone detection.