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Deep learning has had a significant impact on many fields. Recently,code-to-code neural models have been used in code translation, code refinementand decompilation. However, the question of whether these models can automatecompilation has yet to be investigated. In this work, we explore neuralcompilation, building and evaluating Transformer models that learn how toproduce x86 assembler from C code. Although preliminary results are relativelyweak, we make our data, models and code publicly available to encourage furtherresearch in this area.

Learning C to x86 Translation: An Experiment in Neural Compilation