2022 · Conference paper
Augmenting Decompiler Output with Learned Variable Names and Types
Introduces DIRTY, a Transformer model that recommends variable names and types from decompiled code and layout information, together with the DIRT corpus for training and evaluation.
Scope and limitations
The original pipeline is built around C programs and Hex-Rays output. Predicted names and types are learned hypotheses, not guaranteed original symbols. The published paper is from 2022, although its arXiv preprint first appeared in 2021.
Why it fits this collection
Extends name recovery to types, making it directly relevant to assessing semantic annotations added by binary-analysis assistants.
Publication and review details
- Venue
- USENIX Security 2022
- Status
- Conference paper
- Publication / posting date
- August 2022
- Date source
- USENIX proceedings BibTeX publication month
- First arXiv submission
- August 13, 2021
- arXiv
- 2108.06363
- Reviewed arXiv version
- v1
Year and publication month come from the final USENIX conference record; the arXiv date is not substituted for publication.
Sources reviewed
Source review only; experiments were not rerun for this collection. Cite the original authors using the linked paper or BibTeX.