Learning C to X86 Translation: An Experiment in Neural Compilation
Abstract
Deep learning has had a significant impact on many fields. Recently, code-to-code neural models have been used in code translation, code refinement and decompilation. However, the question of whether these models can automate compilation has yet to be investigated. In this work, we explore neural compilation, building and evaluating Transformer models that learn how to produce x86 assembler from C code. Although preliminary results are relatively weak, we make our data, models and code publicly available to encourage further research in this area.
Cite
Text
Armengol-Estapé and O'Boyle. "Learning C to X86 Translation: An Experiment in Neural Compilation." NeurIPS 2021 Workshops: AIPLANS, 2021.Markdown
[Armengol-Estapé and O'Boyle. "Learning C to X86 Translation: An Experiment in Neural Compilation." NeurIPS 2021 Workshops: AIPLANS, 2021.](https://mlanthology.org/neuripsw/2021/armengolestape2021neuripsw-learning/)BibTeX
@inproceedings{armengolestape2021neuripsw-learning,
title = {{Learning C to X86 Translation: An Experiment in Neural Compilation}},
author = {Armengol-Estapé, Jordi and O'Boyle, Michael},
booktitle = {NeurIPS 2021 Workshops: AIPLANS},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/armengolestape2021neuripsw-learning/}
}