Decaf: Improving Neural Decompilation with Automatic Feedback and Search
Decompilers are useful tools used in reverse engineering to understand compiled source code. Reconstructing source code from compiled binaries is a challenging task, because high-level syntax, identifiers, and custom data types are generally lost as the compiler translates human-readable code to low-level machine code. Deterministic decompilers are useful tools for binary analysis, but can struggle to infer idiomatic syntax and identifier names. Generative AI models are a natural fit for reconstructing high-level syntax, identifiers, and types, but they can still suffer by hallucinating improper programming constructs and semantics. Instead of attempting to improve neural decompilers with...
| Authors | Alexander Shypula, Osbert Bastani, Edward Schwartz |
|---|---|
| Primary category | cs.SE |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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