mlsanityData to 14 Oct 2026

Augmenting Decompiler Output with Learned Variable Names and Types

arXiv 2108.06363 ↗First posted 13 Aug 2021cs.SE cs.PLcited by 23

Not on the map. Why?

Abstract (arXiv)

A common tool used by security professionals for reverse-engineering binaries found in the wild is the decompiler. A decompiler attempts to reverse compilation, transforming a binary to a higher-level language such as C. High-level languages ease reasoning about programs by providing useful abstractions such as loops, typed variables, and comments, but these abstractions are lost during compilation. Decompilers are able to deterministically reconstruct structural properties of code, but comments, variable names, and custom variable types are technically impossible to recover. In this paper we present DIRTY (DecompIled variable ReTYper), a novel technique for improving the quality of decompiler output that automatically generates meaningful variable names and types. Empirical evaluation on a novel dataset of C code mined from GitHub shows that DIRTY outperforms prior work approaches by a sizable margin, recovering the original names written by developers 66.4% of the time and the original types 75.8% of the time.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Aug 2021.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 6; 2025: 3; 2026: 9.

23 papers.

Sort:

By area:

  1. 2024LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoningcited by 59
  2. 2023Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Modelscited by 13
  3. 2023Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learningcited by 12
  4. 2024Assemblage: Automatic Binary Dataset Construction for Machine Learningcited by 12
  5. 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Basescited by 8
  6. 2024CodeArt: Better Code Models by Attention Regularization When Symbols Are Lackingcited by 7
  7. 2022Pre-Training Representations of Binary Code Using Contrastive Learningcited by 6
  8. 2023Exploring the Limits of ChatGPT in Software Security Applicationscited by 6
  9. 2024BLens: Contrastive Captioning of Binary Functions using Ensemble Embeddingcited by 3
  10. 2025Fast, Fine-Grained Equivalence Checking for Neural Decompilerscited by 2
  11. 2023Revisiting Deep Learning for Variable Type Recoverycited by 1
  12. 2025A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysiscited by 1
11 more
  1. 2026Fool Me If You Can: On the Robustness of Binary Code Similarity Detection Models against Semantics-preserving Transformationscited by 1
  2. 2026REBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version)cited by 1
  3. 2026Decaf: Improving Neural Decompilation with Automatic Feedback and Searchcited by 1
  4. 2026REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Namingcited by 1
  5. 2024JaxDecompiler: Redefining Gradient-Informed Software Designcited by 0
  6. 2025Asm2SrcEval: Evaluating Large Language Models for Assembly-to-Source Code Translationcited by 0
  7. 2026PCodeTrans: Translate Decompiled Pseudocode to Compilable and Executable Equivalentcited by 0
  8. 2026SoK: AI-Augmented Binary Reversingcited by 0
  9. 2026Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineeringcited by 0
  10. 2026Reveree: Diagnosing LLM Reverse-Engineering Agentscited by 0
  11. 2026When LLM Decompilers Recompile More and Preserve Lesscited by 0