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%.
InProceedings of the 2013 International Conference on Software Engineering (ICSE)
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AI-assisted code review in student projects increased iterative activity and supported code quality discussions while preserving engagement levels across two cohorts.
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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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AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
AI-assisted code review in student projects increased iterative activity and supported code quality discussions while preserving engagement levels across two cohorts.