  Maintaining legacy software requires many software and systems engineering
hours. Assembly code programs, which demand low-level control over the computer
machine state and have no variable names, are particularly difficult for humans
to analyze. Existing conventional program translators guarantee correctness,
but are hand-engineered for the source and target programming languages in
question. Learned transpilation, i.e. automatic translation of code, offers an
alternative to manual re-writing and engineering efforts. Automated symbolic
program translation approaches guarantee correctness but struggle to scale to
longer programs due to the exponentially large search space. Their rigid
rule-based systems also limit their expressivity, so they can only reason about
a reduced space of programs. Probabilistic neural language models (LMs) produce
plausible outputs for every input, but do so at the cost of guaranteed
correctness. In this work, we leverage the strengths of LMs and symbolic
solvers in a neurosymbolic approach to learned transpilation for assembly code.
Assembly code is an appropriate setting for a neurosymbolic approach, since
assembly code can be divided into shorter non-branching basic blocks amenable
to the use of symbolic methods. Guess & Sketch extracts alignment and
confidence information from features of the LM then passes it to a symbolic
solver to resolve semantic equivalence of the transpilation input and output.
We test Guess & Sketch on three different test sets of assembly transpilation
tasks, varying in difficulty, and show that it successfully transpiles 57.6%
more examples than GPT-4 and 39.6% more examples than an engineered transpiler.
We also share a training and evaluation dataset for this task.

Software Engineering;Machine Learning;Programming Languages