Heimdall automates translation of eBPF C programs to Rust with formal equivalence proofs for 94.1% of 102 tested programs using LLMs, static analysis, and Z3-based checking.
Evoc2rust: A skeleton-guided framework for project-level c-to-rust translation
12 Pith papers cite this work. Polarity classification is still indexing.
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ClassEval-Pro benchmark shows frontier LLMs achieve at most 45.6% Pass@1 on class-level code tasks, with logic errors (56%) and dependency errors (38%) as dominant failure modes.
SWE-QA creates a new repository-level code QA benchmark with 576 pairs and an agentic LLM framework, showing promise but open challenges for models handling complex codebases.
AdaTrans uses strategy-driven RAG, error-stratified transformation, and multi-stage validation to reach 95.51% mean compilation pass rate and 81.09% solve rate on 104 algorithmic problems with only 1.19% unsafe files.
Reboot translates six C interpreters (6k-23k LOC) to safe Rust with 1-11 interventions each, passing all original tests and 62-92% on new validation tests while removing memory vulnerabilities.
ENCRUST decouples C-to-Rust translation via ABI wrappers and agentic refinement to reduce unsafe constructs across 15 real programs while preserving full test correctness.
SafeTrans achieves up to 80% successful C-to-Rust translations via LLM iterative repair on 2653 programs and two real projects, with some C vulnerabilities carrying over to the Rust output.
Multisage uses multi-semantic augmentation and self-calibration to enhance LLM-based code translation, achieving up to 2.22x higher success rates on HumanEval-X.
LAC2R uses MCTS to systematically explore multiple LLM refinement trajectories for C-to-Rust translation and reports superior safety and correctness on small-scale benchmarks.
A survey of methods, benchmarks, and open challenges for large language models in multilingual code generation and translation.
citing papers explorer
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Heimdall: Formally Verified Automated Migration of Legacy eBPF Programs to Rust
Heimdall automates translation of eBPF C programs to Rust with formal equivalence proofs for 94.1% of 102 tested programs using LLMs, static analysis, and Z3-based checking.
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ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation
ClassEval-Pro benchmark shows frontier LLMs achieve at most 45.6% Pass@1 on class-level code tasks, with logic errors (56%) and dependency errors (38%) as dominant failure modes.
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SWE-QA: Can Language Models Answer Repository-level Code Questions?
SWE-QA creates a new repository-level code QA benchmark with 576 pairs and an agentic LLM framework, showing promise but open challenges for models handling complex codebases.
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AdaTrans: Automated C to Rust Transformation via Error-Adaptive Repair
AdaTrans uses strategy-driven RAG, error-stratified transformation, and multi-stage validation to reach 95.51% mean compilation pass rate and 81.09% solve rate on 104 algorithmic problems with only 1.19% unsafe files.
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Mostly Automatic Translation of Language Interpreters from C to Safe Rust
Reboot translates six C interpreters (6k-23k LOC) to safe Rust with 1-11 interventions each, passing all original tests and 62-92% on new validation tests while removing memory vulnerabilities.
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ENCRUST: Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation
ENCRUST decouples C-to-Rust translation via ABI wrappers and agentic refinement to reduce unsafe constructs across 15 real programs while preserving full test correctness.
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SafeTrans: LLM-assisted Transpilation from C to Rust
SafeTrans achieves up to 80% successful C-to-Rust translations via LLM iterative repair on 2653 programs and two real projects, with some C vulnerabilities carrying over to the Rust output.
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Enhancing LLM-Based Code Translation with Verified Multi-Semantic Representations
Multisage uses multi-semantic augmentation and self-calibration to enhance LLM-based code translation, achieving up to 2.22x higher success rates on HumanEval-X.
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Search-Based Multi-Trajectory Refinement for Safe C-to-Rust Translation with Large Language Models
LAC2R uses MCTS to systematically explore multiple LLM refinement trajectories for C-to-Rust translation and reports superior safety and correctness on small-scale benchmarks.
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Large Language Models for Multilingual Code Intelligence: A Survey
A survey of methods, benchmarks, and open challenges for large language models in multilingual code generation and translation.
- ReCodeAgent: A Multi-agent Workflow for Language-Agnostic Translation and Validation of Large-Scale Repositories
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