Translating machine code to human-readable high-level languages

Establish effective techniques for translating machine code into human-readable high-level programming languages in the context of reverse engineering.

Background

Classical decompilers often yield functionally correct but hard-to-read outputs lacking meaningful identifiers and idioms, motivating research into more human-readable decompilation. Recent machine learning approaches have improved decompilation to C, but translating low-level machine code into high-level, idiomatic code across languages remains a fundamental challenge.

This paper investigates small, specialized LLMs for decompilation into modern languages (Dart/Swift), evaluating readability with CodeBLEU and syntax validity with compile@k. While the study presents promising results for Dart, the overarching challenge of reliably translating machine code to human-readable high-level code is identified as an open research problem.