Learning C to x86 Translation: An Experiment in Neural Compilation
This addresses the problem of automating compilation for software developers, but it is incremental as it builds on existing code-to-code neural models.
The paper tackled the problem of automating compilation by exploring neural models to translate C code to x86 assembler, with preliminary results being relatively weak and no concrete numbers provided.
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.