Project structure

• EmbeddingLearning: Calculate the embedding features of the AST, CFG, and PDG code representation
• experiment: Store the source code and the corresponding features for experiment
• script: The train and test python script of the DNN model
• src: The code clone detection tool with the source code and the corresponding features
• Dockerfile: To construct the code clone detection tool

Usage

Generate the embedding features

The embedding features of the data set have been created and store here.

Run clone detection

docker run --rm -i -v /mnt/share/CloneData/data/experiment:/workspace cdetector:latest -F1 /workspace/src/1/1.c -F2 /workspace/src/1/2.c

View specific feature by feature id, {file path}:{feature id}

/workspace/src/1/1.c:0

Exit

exit