Beyond Lexical Consistency: Preserving Semantic Consistency for Program Translation

This repo has the PyTorch implementation and datasets of our ICDM 2023 paper: Beyond Lexical Consistency: Preserving Semantic Consistency for Program Translation

Introduction

Program Translation aims to convert the input programs from one programming language to another. Automatic program translation is a prized target of software engineering research, which leverages the reusability of projects and improves the efficiency of development. Recently, thanks to the rapid development of deep learning model architectures and the availability of large-scale parallel corpus of programs, the performance of program translation has been greatly improved. However, the existing program translation models are still far from satisfactory, in terms of the quality of translated programs. In this paper, we argue that a major limitation of the current approaches is that they only consider lexical consistency in program translation, but misses semantic consistency, which is also critical for the task. To make the program translation model more semantically aware, we propose a general framework named Preserving Semantic Consistency for Program Translation(PSCPT), which considers semantic consistency with regularization in the training objective of program translation and can be easily applied to all encoder-decoder methods with various neural networks(e.g., LSTM, Transformer) as the backbone. We conduct extensive experiments in 7 general programming languages. Experimental results show that with CodeBERT as the backbone, our approach outperforms not only the state-of-the-art open-source models but also the commercial closed large language model(e.g., text-davinci-002, text-davinci-003) on the program translation task.

Requirements

• Conda
  • install conda: https://conda.io/projects/conda/en/latest/user-guide/install/index.html
  • create a new conda environment:
    • if you are running with GPU:

      conda env create -f environment-gpu.yml
      conda activate mqmc
      

      Dependencies include support for CUDA_11.4. If you are using a different CUDA version update the dependencies accordingly.
    • if you are running with CPU:

      conda env create -f environment-cpu.yml
      conda activate mqmc
      

Example to Run the Codes

The instruction of commands has been clearly stated in the codes.

• Java-C#

python run.py --model_type='roberta' --output_dir='saved_models/Java-C#/' --train_filename='../data/program_data/Java-C#/train-Java-C#-tok.cs,../data/program_data/Java-C#/train-Java-C#-tok.java,../data/map_data/C#-program-tok.jsonl,../data/program_data/Java-C#/train-C#-map.jsonl@' --test_filename='../data/program_data/Java-C#/test-Java-C#-tok.cs,../data/program_data/Java-C#/test-Java-C#-tok.java,../data/map_data/C#-program-tok.jsonl,../data/program_data/Java-C#/test-C#-map.jsonl' --train_snippet_filename=None --dev_snippet_filename=None --test_snippet_filename=None --source_lang='Java' --target_lang='C#' --max_source_length=512 --max_target_length=512 --max_comment_length=64 --do_train --do_eval --do_test --train_batch_size=128 --eval_batch_size=128 --learning_rate=0.0001 --beam_size=10 --weight_decay=0.0 --adam_epsilon=1e-08 --max_grad_norm=1.0 --num_train_epochs=200 --max_steps=-1 --eval_steps=-1 --train_steps=-1 --warmup_steps=0 --local_rank=-1 --seed=42 --temperature=2.0 --weightAB=1.0 --weightBB=2.0 --weightcon=3.0 --autonum=50

Contact

If You find any problems or have any questions, please contact me.

Acknowledgments and Licenses

• The calculation of CodeBLEU in our work is adapted from CodeBLEU (https://github.com/microsoft/CodeXGLUE/).
• The public CosT dataset used in our work is from MuST-CoST (https://github.com/reddy-lab-code-research/MuST-CoST/)
• All license clauses are in the LICENSE file.