FlyCatcher infers 300 correct stateful runtime checkers from 400 tests across four systems, yielding 2.6x more correct checkers and 5.2x more error detections than prior work.
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MOA deploys LLM agents to detect recurring memory anti-patterns via profiling, synthesize static analyzers, and apply patches, reporting 42% heap and 11% binary-size reductions on OpenHarmony after finding over 10,000 issues.
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FlyCatcher: Neural Inference of Runtime Checkers from Tests
FlyCatcher infers 300 correct stateful runtime checkers from 400 tests across four systems, yielding 2.6x more correct checkers and 5.2x more error detections than prior work.
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MOA: A Profiling-Guided LLM Framework for Memory-Optimization Automation at Codebase Scale
MOA deploys LLM agents to detect recurring memory anti-patterns via profiling, synthesize static analyzers, and apply patches, reporting 42% heap and 11% binary-size reductions on OpenHarmony after finding over 10,000 issues.