The code for the paper Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving.
@article{10.1145/3622825,
author = {Ye, Fangke and Zhao, Jisheng and Shirako, Jun and Sarkar, Vivek},
title = {Concrete Type Inference for Code Optimization Using Machine Learning with SMT Solving},
year = {2023},
issue_date = {October 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {7},
number = {OOPSLA2},
url = {https://doi.org/10.1145/3622825},
doi = {10.1145/3622825},
journal = {Proc. ACM Program. Lang.},
month = {oct},
articleno = {249},
numpages = {28}
}
A C++ compiler that supports C++14 is required.
It is recommended to use a conda environment to install the Python dependencies:
conda create -n cti python=3.9
conda activate cti
pip install -r requirements.txtNote that the cvc5 package specified in requirements.txt may not be available for all platforms. If the installation fails, please refer to cvc5's official website for installation instructions.
The PyTorch and torch-scatter packages included in requirements.txt are for CPU only and are used for model inference in the experiments. To train our deep learning models using a GPU, please refer to the websites of PyTorch and torch-scatter for instructions on how to install the GPU version.
Please see the benchmarks directory.
For the SciPy dataset and the Freq, DeepTyper-FS, and CodeT5-FT models, please refer to the training directory.
For the GPT-4 model, please refer to the gpt4 directory.
We provide a modified version of the Intrepydd compiler introduced in the Intrepydd paper. Please refer to the intrepydd directory for more details.