Supersonic: Learning to Generate Source Code Optimisations in C/C++

This is the GitHub repository for Supersonic: Learning to Generate Source Code Optimisations in C/C++ (IEEE TSE, 2024).

@article{supersonic,
 title = {Supersonic: Learning to Generate Source Code Optimizations in C/C++},
 journal = {IEEE Transactions on Software Engineering},
 year = {2024},
 doi = {10.1109/TSE.2024.3423769},
 author = {Zimin Chen and Sen Fang and Martin Monperrus},
 url = {http://arxiv.org/pdf/2309.14846},
}

The model is uploaded to Zenodo, and the filtered dataset used to train Supersonic is upladed to Zenodo. The unfiltered dataset is also uploaded to Zenodo.

Predictions

All Supersonic, GPT-3.5-Turbo and GPT-4 predictions are located in predictions/.

Files

All executable file uses argparse, meaning that you can run python foo.py -h to see all command line arguments

src/codeforces-crawler

• src/codeforces-crawler/crawler.py: Uses Codeforces API to crawl submission information.
• src/codeforces-crawler/find_submission_code.py: Uses dataset from this blog, and finds the actual code for each Codeforces submission.

src/codenet-crawler

• src/codenet-crawler/parse_subnet_csv.py: First download the Codenet dataset from their GitHub page. Then run this file to find all submission information.
• src/codenet-crawler/find_submission_code.py: Finds the actual code for each Codenet submission.

src/code-preprocessing

• src/code-preprocessing/find_improvement_submissions.py: Using the Codeforces and Codenet submissions, we find submission where either the time or memory improved.
• src/code-preprocessing/format_code.py: Uses gcc to remove source code comment and clang-format to format all submissions.

src/train

• src/train/generate_huggingface_dataset.py: Generates dataset to be used for huggingface models. There are several filters defined in this file.
• src/train/generate_bert2bert_sweep_config.py: Generates bert2bert training files.
• src/train/generate_codet5_sweep_config.py: Generates codet5 training files.

src/evaluation

• src/evaluation/huggingface_inference.py: Generate predictions on the test dataset.
• src/evaluation/compare.py: Compute the test accuracy.
• src/evaluation/generate_predicted_submissions.py: Parse the model output and generate the acutual predicted program. We need this step since the model predicts a diff.
• src/evaluation/output_submission_dir.py: Generate a directory containing all predicted submissions.

src/submit_code

• src/submit_code/aizu_submit.py: Upload generated solutions to AIZU.
• src/submit_code/atcoder_submit.py: Upload generated solutions to AtCoder.
• src/submit_code/codeforces_submit.py: Upload generated solutions to Codeforces.

src/notebooks

• src/notebooks/Statistics.ipynb: The statistics notebook.
• src/notebooks/figures.ipynb: Notebook to generate figures used in the paper.