@inproceedings{zhou-etal-2026-sactor,
    title = "{SACTOR}: {LLM}-Driven Correct and Idiomatic {C} to Rust Translation with Static Analysis and {FFI}-Based Verification",
    author = "Zhou, Tianyang  and
      Zhang, Ziyi  and
      Lin, Haowen  and
      Jha, Somesh  and
      Christodorescu, Mihai  and
      Levchenko, Kirill  and
      Chandrasekaran, Varun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.28/",
    pages = "646--673",
    ISBN = "979-8-89176-390-6",
    abstract = "Translating software written in C to Rust has significant benefits in improving memory safety. However, manual translation is cumbersome, error-prone, and often produces unidiomatic code. Large language models (LLMs) have demonstrated promise in producing idiomatic translations, but offer no correctness guarantees. We propose SACTOR, an LLM-driven C-to-Rust translation tool that employs a two-step process: an initial ``unidiomatic'' translation to preserve semantics, followed by an ``idiomatic'' refinement to align with Rust standards. SACTOR leverages static analysis of the C source to handle pointer semantics and dependency resolution. To validate correctness of our function-wise incremental translation that mixes C and Rust, we use end-to-end testing via the foreign function interface. We evaluate SACTOR on 200 programs from two public datasets and on two more complex scenarios (a 50-sample subset of CRust-Bench and the libogg library), comparing multiple LLMs. Across datasets, SACTOR delivers high end-to-end correctness and produces safe, idiomatic Rust with up to 7$\times$ fewer Clippy warnings; On CRust-Bench, SACTOR achieves an average (across samples) of 85{\%} unidiomatic and 52{\%} idiomatic success, and on libogg it attains full unidiomatic and up to 78{\%} idiomatic coverage on GPT-5."
}