  We describe and evaluate LF-checker, a metaverifier tool based on machine
learning. It extracts multiple features of the program under test and predicts
the optimal configuration (flags) of a bounded model checker with a decision
tree. Our current work is specialised in concurrency verification and employs
ESBMC as a back-end verification engine. In the paper, we demonstrate that
LF-checker achieves better results than the default configuration of the
underlying verification engine.

Machine Learning