mlsanityData to 14 Oct 2026

CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code

arXiv 2310.16853 ↗First posted 24 Oct 2023cs.PL cs.AIcited by 11

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Abstract (arXiv)

Automatically generating function summaries for binaries is an extremely valuable but challenging task, since it involves translating the execution behavior and semantics of the low-level language (assembly code) into human-readable natural language. However, most current works on understanding assembly code are oriented towards generating function names, which involve numerous abbreviations that make them still confusing. To bridge this gap, we focus on generating complete summaries for binary functions, especially for stripped binary (no symbol table and debug information in reality). To fully exploit the semantics of assembly code, we present a control flow graph and pseudo code guided binary code summarization framework called CP-BCS. CP-BCS utilizes a bidirectional instruction-level control flow graph and pseudo code that incorporates expert knowledge to learn the comprehensive binary function execution behavior and logic semantics. We evaluate CP-BCS on 3 different binary optimization levels (O1, O2, and O3) for 3 different computer architectures (X86, X64, and ARM). The evaluation results demonstrate CP-BCS is superior and significantly improves the efficiency of reverse engineering.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 24 Oct 2023.

Builds on

15 of 39 references.

References · 15

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  11. 2020Improved Code Summarization via a Graph Neural Networkcited by 79
  12. 2021CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Treescited by 32
3 more
  1. 2020Code Summarization with Structure-induced Transformercited by 29
  2. 2023Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarizationcited by 6
  3. 2022A Neural Network Architecture for Program Understanding Inspired by Human Behaviorscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 4; 2026: 1.

11 papers.

  1. 2023Exploring ChatGPT's Capabilities on Vulnerability Managementcited by 19
  2. 2025CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent Systemcited by 15
  3. 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Basescited by 8
  4. 2024MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarizationcited by 5
  5. 2024BLens: Contrastive Captioning of Binary Functions using Ensemble Embeddingcited by 3
  6. 2025How Different Tokenization Algorithms Impact LLMs and Transformer Models for Binary Code Analysiscited by 3
  7. 2025FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generationcited by 2
  8. 2024A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfercited by 1
  9. 2024A Survey on the Real Power of ChatGPTcited by 0
  10. 2025C-ing Clearly: Enhanced Binary Code Explanations using C codecited by 0
  11. 2026SoK: AI-Augmented Binary Reversingcited by 0