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Canonical funds three-year research project to automate translation of large C codebases into safe Rust
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Canonical is partnering with the University of Bristol on a three-year effort to build a neurosymbolic platform that can automatically convert large, mature C projects into safe Rust, using AppArmor and snap-confine as test cases.
Automated C-to-Rust translation could reduce the cost and risk of modernising legacy codebases, improving memory safety and maintainability. By combining machine learning with formal analysis, the project aims to produce idiomatic Rust without the extensive manual rewrites that current approaches require.
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The project will develop a hybrid neurosymbolic translator that splits large repositories, uses language models trained on C-to-Rust examples, and validates the Rust output against original C behavior.
Canonical will use Ubuntu components AppArmor and snap-confine as real-world case studies, though they will not be rewritten in Rust during the research.
The effort targets shortcomings of existing source-to-source tools and LLMs, which either retain unsafe C patterns or cannot manage repository-wide context.
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