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Code clone detection is about finding out similar code fragments, which hasdrawn much attention in software engineering since it is important for softwaremaintenance and evolution. Researchers have proposed many techniques and toolsfor source code clone detection, but current detection methods concentrate onanalyzing or processing code samples individually without exploring theunderlying connections among code samples. In this paper, we propose Gitor tocapture the underlying connections among different code samples. Specifically,given a source code database, we first tokenize all code samples to extract thepre-defined individual information. After obtaining all samples individualinformation, we leverage them to build a large global sample graph where eachnode is a code sample or a type of individual information. Then we apply a nodeembedding technique on the global sample graph to extract all the samplesvector representations. After collecting all code samples vectors, we cansimply compare the similarity between any two samples to detect possible clonepairs. More importantly, since the obtained vector of a sample is from a globalsample graph, we can combine it with its own code features to improve the codeclone detection performance. To demonstrate the effectiveness of Gitor, weevaluate it on a widely used dataset namely BigCloneBench. Our experimentalresults show that Gitor has higher accuracy in terms of code clone detectionand excellent execution time for inputs of various sizes compared to existingstate-of-the-art tools. Moreover, we also evaluate the combination of Gitorwith other traditional vector-based clone detection methods, the results showthat the use of Gitor enables them detect more code clones with higher F1.

Gitor: Scalable Code Clone Detection by Building Global Sample Graph