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Enterprise AI Analysis: Project-Level C-to-Rust Translation via Pointer Knowledge Graphs

PROJECT-LEVEL C-TO-RUST TRANSLATION VIA POINTER KNOWLEDGE GRAPHS

A novel C-Rust Pointer KG and synergy with LLMs to translate C projects into safe and idiomatic Rust.

Executive Impact & Key Performance Indicators

Our analysis reveals significant advancements and tangible results achieved through optimized AI integration.

94.9% Lint Warning Reduction
29.3% Higher Functional Correctness (LoC < 4K)
53.7% Higher Functional Correctness (LoC 4K-15K)

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Our approach constructs a C-Rust Pointer KG for the given C project. In addition to a basic code-dependency graph which preserves relationships among code units, our C-Rust Pointer KG further incorporates two categories of C-Rust pointer semantics: (i) pointer-usage information which records global pointer behaviors, and (ii) Rust-oriented annotations which encode explicit Rust-oriented pointer labels.

Our C-Rust Pointer KG builds on a code-dependency graph as the structural backbone, and is further enriched with C-Rust pointer semantics, including pointer-usage information and Rust-oriented annotations. In particular, our KG focuses on the pointers in function parameters, function return values, and members of structs and unions, as these pointers define module interfaces, which are crucial for inter-module semantic consistency.

PTRTRANS leverages the constructed C-Rust Pointer KG to guide LLMs to automatically translate a given C project into Rust. Unlike existing approaches that rely solely on LLMs to infer implicit pointer semantics knowledge in C code [7, 17, 21, 32, 58, 60], PTRTRANS leverages explicit pointer semantics from the pointer KG to assist LLMs in generating idiomatic Rust code.

During error correction, PTRTRANS leverages the Rust copy of our pointer KG to provide LLMs with project-level semantic context, rather than relying solely on compiler error messages as in prior methods [7, 17, 32, 44, 58, 60]. The KG assists repair in two main ways: (1) its dependency relations enable precise identification of code units involved in a given error, clarifying inter-component interactions; and (2) each node is enriched with Rust-oriented annotations (e.g., ownership and mutability), which supply precise program semantics that guide LLMs toward effective fixes and prevent them from getting stuck in unproductive repair loops.

99.9% Reduction in Unsafe Code Usage

PtrTrans Workflow

KG Construction
KG-Guided Translation
KG-Guided Correction
Fixed Rust Code
94.9% Lint Warning Reduction (%)
29.3% Higher Functional Correctness (%) (LoC < 4K)
53.7% Higher Functional Correctness (%) (LoC 4K-15K)

PtrTrans vs. Baselines

Feature PtrTrans Conventional LLM-Based
Global Pointer Semantics
  • Explicitly models C-Rust Pointer KG
  • Lacks global view
Unsafe Code Reduction
  • 99.9%
  • Often generates unsafe blocks
Functional Correctness
  • Higher by 29.3-53.7%
  • Prone to borrow conflicts

Impact on `urlparser` and `libtree`

Manual investigation shows that functions with translation errors generally require minimal manual effort.

Average CodeBLEU scores of 0.88 for both `urlparser` and `libtree` indicate small-scale adjustments are needed.

Average DSR@k is 1.0 for `urlparser` and 1.3 for `libtree`, implying most functions are correctly repaired in one or two attempts.

For `urlparser`, 12 unit tests fail, but only 2 functions require actual repair. The function `get_part` is invoked by other 10 functions that all fail on tests; once `get_part` is repaired, these 10 functions pass without further modifications.

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Phase 2: Solution Design & Development

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Phase 3: Integration & Deployment

Seamless integration of the AI solution into your enterprise ecosystem, followed by rigorous testing and controlled deployment.

Phase 4: Optimization & Scaling

Continuous monitoring, performance tuning, and scalable expansion of the AI solution to maximize long-term value and impact.

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