mlsanityData to 14 Oct 2026

His2Trans: A Knowledge-Guided Agentic Framework for Project-Level C-to-Rust Migration

arXiv 2603.02617 ↗First posted 3 Mar 2026cs.SEcited by 1

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

C remains a major implementation language for operating systems, embedded platforms, and infrastructure software, but manual memory management continues to create security and maintenance costs. Rust is a practical migration target because it retains low-level control while enforcing stronger memory-safety checks. At project scale, especially under gradual C/Rust coexistence, migration is not a sequence of syntax-preserving function rewrites. A translator must preserve project interfaces, observable behavior, system interaction protocols, and low-level interoperability boundaries while staying consistent with migration choices already made in the codebase. We introduce His2Trans, a knowledge-guided agentic framework for project-level C-to-Rust migration. His2Trans reuses interface-level and fragment-level knowledge mined from historical C/Rust migrations to guide new translations toward Rust interfaces, wrapper choices, and local idioms already accepted in the evolving project. It then refines the assembled crate with project-level agentic feedback. On ten OpenHarmony modules, His2Trans reaches a 100.00% incremental compilation pass rate, a 94.92% Test Pass Rate, and a 16.35% Unsafe Ratio. On eight open-source C projects, it reaches 100.00% for both incremental compilation and Test Pass Rate, reducing Unsafe Ratio from 42.88% under C2Rust to 8.59%. These results support knowledge-guided migration and project-level agentic refinement as practical mechanisms for preserving observable behavior while reducing the unsafe burden of rule-based transpilation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 19 of its 41 references that are in the map. First posted on arXiv 3 Mar 2026.

Builds on

19 of 41 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation4 Oct

Other references · 18

  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  2. 2024Exploring and Unleashing the Power of Large Language Models in Automated Code Translationcited by 57
  3. 2024Towards Translating Real-World Code with LLMs: A Study of Translating to Rustcited by 35
  4. 2025jina-reranker-v3: Last but Not Late Interaction for Listwise Document Rerankingcited by 24
  5. 2024VERT: Verified Equivalent Rust Transpilation with Large Language Models as Few-Shot Learnerscited by 20
  6. 2025EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translationcited by 19
  7. 2025C2SaferRust: Transforming C Projects into Safer Rust with NeuroSymbolic Techniquescited by 18
  8. 2023Ownership guided C to Rust translationcited by 13
  9. 2025RustMap: Towards Project-Scale C-to-Rust Migration via Program Analysis and LLMcited by 10
  10. 2025SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verificationcited by 9
  11. 2024To Tag, or Not to Tag: Translating C's Unions to Rust's Tagged Unionscited by 8
  12. 2025SafeTrans: LLM-assisted Transpilation from C to Rustcited by 8
6 more
  1. 2025Integrating Rules and Semantics for LLM-Based C-to-Rust Translationcited by 6
  2. 2025Translating Large-Scale C Repositories to Idiomatic Rustcited by 6
  3. 2024SmartC2Rust: Iterative, Feedback-Driven C-to-Rust Translation via Large Language Models for Safety and Equivalencecited by 5
  4. 2025Forcrat: Automatic I/O API Translation from C to Rust via Origin and Capability Analysiscited by 5
  5. 2025LLMigrate: Transforming "Lazy" Large Language Models into Efficient Source Code Migratorscited by 4
  6. 2025Search-Based Multi-Trajectory Refinement for Safe C-to-Rust Translation with Large Language Modelscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

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  2. 2026Towards Reliable C-to-Rust Translation with Rule-Guided Reasoning and Reinforcement Learning
  3. 2026From C to Idiomatic Rust: A Ship-of-Theseus Agentic Translation ↗
  4. 2026ENCRUST: Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation
  5. 2026Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT
  6. 2026Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents
  7. 2026From Translation to Superset: Benchmark-Driven Evolution of a Production AI Agent from Rust to Python
  8. 2026AkiraRust: Re-thinking LLM-aided Rust Repair Using a Feedback-guided Thinking Switch