LiteCoder/LiteCoder_SourceCode

LiteCoder Experiment Reproducing package

• —To run the pre-train objective use the following scripts:

• —Reproduce LiteCoder with all objectives:

• —Navigate the folder Pre-training containing the LiteCoder.py file
• —Then, run Python LiteCoder.py --train-tt --train-cs --train-pd

• —The pretrained model is released on hugging face, therefore it automatically loads.

• —To run the ablation studies:

• —Ablation 1: Python LiteCoder.py --train-tt
• —Ablation 2: Python LiteCoder.py --train-tt --train-cs
• —Ablation 3: Python LiteCoder.py --train-tt --train-cs --train-pd

• —To Fine-tuning LiteCoder on downstream tasks:

• —Navigate to the Fine-tuning folder and then Downstream task folder:

• —Code Clone Detection:
• —Follow the instruction of readme.md file.

• —Code Translation:

• —Run setup.sh file.
• —Navigate to the scripts/finetune and run translate.sh file.

• —To extract the programming language features (i.e., token type, code sememe, and code dependencies)
• —We used open source datasets to extract language features. we released the extracted datasets on the Hugging Face:
• —LT_Java : LiteCoder/LT_Java
• —LT_Python : LiteCoder/LT_Python
• —LT_Java_Dependency : LiteCoder/LT_Java_Dependency

• —Navigate to the utils directory:
• —Use either the Java or Python notebook file to run over your dataset.
• —Run the cells, for which, you want to extract the features.

• —Dependencies:
• —Feature extraction dependencies:

    - pip install ast-comments
    - pip install ast
    - pip install javalang
    - pip install tree-sitter
    
  - Model training dependencies:

• —pip install transformers
• —pip install datasets
• —pip install pytorch_lightning
• —pip install torch

• —Or pip install -r requirements.txt