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.

Semantic code clone detection is an important yet challenging task in software engineering. Traditional methods rely on expert experience and cannot automatically determine which features are better for semantic code clone detection. Moreover, the program dynamics (e.g., the execution characteristics and execution order of statements) are not considered in these methods. As a result, this limits their ability to detect semantic clones. To address this issue, we propose a code clone detection method based on event embedding tree and Graph Attention Network. Our method uses a program control flow graph to capture the execution characteristics of each statement and extract the context relationship of different statements in the control flow. Based on such information, our method can calculate the functional similarity of two pieces of code, thereby identifying semantically similar code fragments. Experimental results show that our method is superior to state-of-the-art open source methods for Type-3 (syntactic) / Type-4 (semantic) clone detection.