MSR2021-ProgramRepair

Paper

You can find the paper here: https://arxiv.org/abs/2103.11626

Data

data folder contains multiple folders and files:

• repetition folder contains MSR datasets WITH duplicate pairs
• unique folder contains MSR datasets WITHOUT duplicate pairs
• sstubs(Large|Small).json files contain dataset in JSON format
• sstubs(Large|Small)-(train|test|val).json files contain dataset split in JSON format
• split/(large|small) folders contain dataset in text format (what the CodeBERT works with)

Running CodeBERT Experiments

1. Clone the repository
   • git lfs install
   • git clone https://github.com/EhsanMashhadi/MSR2021-ProgramRepair.git
2. Download the CodeBERT model
   • cd MSR2021-ProgramRepair
   • git clone https://huggingface.co/microsoft/codebert-base
   • use the downloaded model's directory path as pretrained_model variable in script files
3. Install dependencies
   • pip install torch==1.4.0
   • pip install transformers==2.5.0
4. Train the model with MSR data
   • bash ./scripts/codebert/train.sh
5. Evaluate the model
   • bash ./scripts/codebert/test.sh

Running Simple LSTM Experiments

1. Install OpenNMT-py
   • pip install OpenNMT-py==2.2.0
   • If you face conflicts between pytorch and CUDA version, you can follow this link
2. Preprocess the MSR data
   • bash ./scripts/simple-lstm/build_vocab.sh
3. Train the model
   • bash ./scripts/simple-lstm/train.sh
4. Evaluate the model
   • bash ./scripts/simple-lstm/test.sh

Running Simple LSTM Experiments using the legacy version of OpenNMT-py

(This is the original version used to run the simple LSTM experiments in the paper.)

1. Install OpenNMT-py legacy
   • pip install OpenNMT-py==1.2.0
2. Preprocess the MSR data
   • bash ./scripts/simple-lstm/legacy/preprocess.sh
3. Train the model
   • bash ./scripts/simple-lstm/legacy/train.sh
4. Evaluate the model
   • bash ./scripts/simple-lstm/legacy/test.sh

How to run all experiments?

• You can change the size and type variables value in script files to run different experiments (large | small, unique | repetition).

Have trouble running on GPU?

1. Check the CUDA and PyTorch compatibility
2. Assign the correct values for CUDA_VISIBLE_DEVICES, gpu_rank, and world_size based on your GPU numbers in all scripts.
3. Run on GPU by removing the gpu_rank, and world_size options in all scripts.