Implementation for the ICSE 2022 paper "Learning to Recommend Method Names with Global Context"

• We used the same dataset as MNire, Code2vec, and DeepName. All the repos are listed in ./data/repos.txt
  • For in-project setting, we used the same setting as in MNire and code2vec to shuffle files in all the projects and split them into 1.7M training and 61K testing files.
  • For cross-project and low resource setting, we first split 9000/200/1022 projects for training/validation/testing. Then we sample 4000-projects from 9000-training-projects as the final training-set. The repos are listed in ./data/small_train_projects.txt, ./data/eval_projects.txt, ./data/test_projects.txt

1. run merge_project.py to save project information

   --data_path: project data dir
   --save_path: dir to save the project information data (`java-train.pkl, java-eval.pkl, java-test.pkl`)
   

2. run processor.py to get code schema and cross project information

   --input_file: # dir to save the project information data (For example: `data_path/java-train.pkl`)
   --schema_file # dir to save code schema information (For example: `data_path/java-train_schema.pkl`)
   --output_file # dir to save code schema and cross project information (For example: `data_path/java-train_all.pkl`)
   

3. run extract_data.py to save final pickle data

   --sub_vocab_file: vocabulary for subtokens in the source code 
   --doc_vocab_file: vocabulary for documentation of the methods
   --input_file_name: dir to save code schema and cross project information (For example: `data_path/java-train_all.pkl`)
   --output_file_name: dir prefix to save the final data for training and evaluation (For example: `data_path/train_subword`, following files will be saved:  `data_path/train_subword_body/doc/pro/tag.pkl`)
   

4. run invoked_save.py to save invoked mask for project context

   parameters:
   data_path: dir to save the final data for training and evaluation
   prefix: data prefix, for example: train_subword
   

• parameters are configured in hparams.py

run `train.py`

python train.py --gpu gpu_id --pro True

run `test.py`

python test.py --gpu gpu_id --pro True