Duplicated code detecting method based on neural network language model
A language model and code detection technology, applied in biological neural network models, error detection/correction, software testing/debugging, etc., can solve the problems of economic loss of code creators, inability to detect duplicate codes, etc., to protect intellectual property rights, The effect of avoiding dimensional disasters and preventing economic losses
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[0052] Select 260 apps from different Android application markets, each of which is manually analyzed, and the collection of 100 clone codes is also manually determined;
[0053] Use step 1 to convert the code into the corresponding CFG graph;
[0054] For all CFG graphs, use step 2 to obtain the root subgraph of each node;
[0055] Using step 3, the vector representation of each root subgraph is learned;
[0056] Using step 4, the similarity between all CFG graphs is obtained;
[0057] Use step 5 to cluster all the CFG graphs, and the codes corresponding to the CFG graphs in the same cluster are repeated codes. The clustering results are measured by the ARI index, and its value is 0.88.
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