# DIRE: A Neural Approach to Decompiled Identifier Naming

arXiv: https://arxiv.org/abs/1909.09029 · HTML: https://mlsanity.com/paper/1909.09029/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 30 of 36)

- [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021)
- [LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning](https://mlsanity.com/paper/2401.16185/) (2024)
- [LLM4Decompile: Decompiling Binary Code with Large Language Models](https://mlsanity.com/paper/2403.05286/) (2024)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries](https://mlsanity.com/paper/2301.01701/) (2023)
- [CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision](https://mlsanity.com/paper/2402.16928/) (2024)
- [Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning](https://mlsanity.com/paper/2311.13721/) (2023)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning](https://mlsanity.com/paper/2112.02650/) (2021)
- [Boosting Neural Networks to Decompile Optimized Binaries](https://mlsanity.com/paper/2301.00969/) (2023)
- [Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases](https://mlsanity.com/paper/2405.19581/) (2024)
- [Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling](https://mlsanity.com/paper/2103.12801/) (2021)
- [Pre-Training Representations of Binary Code Using Contrastive Learning](https://mlsanity.com/paper/2210.05102/) (2022)
- [Exploring the Limits of ChatGPT in Software Security Applications](https://mlsanity.com/paper/2312.05275/) (2023)
- [XFL: Naming Functions in Binaries with Extreme Multi-label Learning](https://mlsanity.com/paper/2107.13404/) (2021)
- [ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding](https://mlsanity.com/paper/2504.00019/) (2025)
- [Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules](https://mlsanity.com/paper/2109.10476/) (2021)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement](https://mlsanity.com/paper/2406.17233/) (2024)
- [BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding](https://mlsanity.com/paper/2409.07889/) (2024)
- [Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models](https://mlsanity.com/paper/2106.02452/) (2021)
- [Fast, Fine-Grained Equivalence Checking for Neural Decompilers](https://mlsanity.com/paper/2501.04811/) (2025)
- [BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software](https://mlsanity.com/paper/2509.25248/) (2025)
- [Revisiting Deep Learning for Variable Type Recovery](https://mlsanity.com/paper/2304.03854/) (2023)
- [AGNOMIN -- Architecture Agnostic Multi-Label Function Name Prediction](https://mlsanity.com/paper/2509.25514/) (2025)
- [Context-Guided Decompilation: A Step Towards Re-executability](https://mlsanity.com/paper/2511.01763/) (2025)
- [REBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version)](https://mlsanity.com/paper/2604.27319/) (2026)
- [Decaf: Improving Neural Decompilation with Automatic Feedback and Search](https://mlsanity.com/paper/2605.11501/) (2026)
- [Epicure: Distilling Sequence Model Predictions into Patterns](https://mlsanity.com/paper/2308.08203/) (2023)
- [STraceBERT: Source Code Retrieval using Semantic Application Traces](https://mlsanity.com/paper/2312.04731/) (2023)

## Abstract (arXiv)

The decompiler is one of the most common tools for examining binaries without corresponding source code. It transforms binaries into high-level code, reversing the compilation process. Decompilers can reconstruct much of the information that is lost during the compilation process (e.g., structure and type information). Unfortunately, they do not reconstruct semantically meaningful variable names, which are known to increase code understandability. We propose the Decompiled Identifier Renaming Engine (DIRE), a novel probabilistic technique for variable name recovery that uses both lexical and structural information recovered by the decompiler. We also present a technique for generating corpora suitable for training and evaluating models of decompiled code renaming, which we use to create a corpus of 164,632 unique x86-64 binaries generated from C projects mined from GitHub. Our results show that on this corpus DIRE can predict variable names identical to the names in the original source code up to 74.3% of the time.