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We describe and evaluate LF-checker, a metaverifier tool based on machinelearning. It extracts multiple features of the program under test and predictsthe optimal configuration (flags) of a bounded model checker with a decisiontree. Our current work is specialised in concurrency verification and employsESBMC as a back-end verification engine. In the paper, we demonstrate thatLF-checker achieves better results than the default configuration of theunderlying verification engine.

LF-checker: Machine Learning Acceleration of Bounded Model Checking for Concurrency Verification (Competition Contribution)