The implementation of "Adding Context to Source Code Representations for Deep Learning"

This research is based on the dataset SeSaMe. https://github.com/FAU-Inf2/sesame

• If you want to replicate the experiments in this project, you have to download the SeSaMe dataset and follow its authors' instructions to download the 11 projects.

• You can run the make pull command directly in the directory sesame/src/.

  If you have a problem with this step, go to the original project link above to

  find a solution.

To do the next experiments, you must have jar packages for all the projects in the dataset. You can go one of the following two ways.

• You can manually install them according to the documentation for each project or look for jar releases in a project's github repository.
• If you find the process above cumbersome, you can also use the jar packages stored in the jar_pack folder.

In this research, we use the tool java-callgraph to get the callgraph of source code. For more information, please visit this website. https://github.com/gousiosg/java-callgraph

We obtain the callgraph of each project by concatenating the callgraphs generated by the jar packages of the project. These callgraphs are stored in cg files, which can be viewed as common txt files.