Multi-Location Software Model Completion

Paper

Additional information regarding the paper is given in the PDF file: Multi-Location_Software Model_Completion.pdf of the repository. More precisely, the paper has an appendix there; for more info, just scroll down to the appendix at the very end of the paper.

Code

Code to run is given in experiments/crosscutting_changes.

Preprocessing

To compute the embeddings, run the script preprocessing_add_node_embeddings.py,
which will embed the nodes of the graphs. Please provide your OpenAI key there.

Training

Run neural_network_analyser.py for training the neural network.

Run neural_network_hyperparametertuning.py for hyperparameter tuning.

Run historical_only_analyser.py, which builds the adjacency matrix for the historical baseline from the train set.

Testing

All scripts start with test_eval.
The testing scripts for the baseline semantics, historical, random, and neural network approach "NextFocus" are named test_eval_{approach}.

These will be passed through the network.

Final statistics and eval

Start with final_:

• final_comparison.py for statistics on Experiment 1 and Experiment 3
• final_eval_graph_radius.py for Experiment 2
• final_eval_dataset_size.py for Experiment 3

Additional info

Files starting with Helper are called by the rest.
Modules follow the same pattern.

Data

The data and intermediate results of the approach are for simplicity provided in the data folder.