Reinforcement learning on MIR features combined with cargo-fuzz validation reduces false positives in Rust static memory safety analysis, raising precision from 25.6% to 59.0% and accuracy to 65.2%.
InACM Transactions on Software Engineering and Methodology (TOSEM), Vol
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PtrTrans builds a Pointer Knowledge Graph with points-to flows, struct abstractions, and Rust annotations to guide LLMs toward project-level C-to-Rust translations that cut unsafe code by 99.9% and raise functional correctness by 29.3%.
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Mitigating False Positives in Static Memory Safety Analysis of Rust Programs via Reinforcement Learning
Reinforcement learning on MIR features combined with cargo-fuzz validation reduces false positives in Rust static memory safety analysis, raising precision from 25.6% to 59.0% and accuracy to 65.2%.
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Project-Level C-to-Rust Translation via Pointer Knowledge Graphs
PtrTrans builds a Pointer Knowledge Graph with points-to flows, struct abstractions, and Rust annotations to guide LLMs toward project-level C-to-Rust translations that cut unsafe code by 99.9% and raise functional correctness by 29.3%.