ICE: A Robust Framework for Learning Invariants

  title={ICE: A Robust Framework for Learning Invariants},
  author={Pranav Garg and Christof L{\"o}ding and P. Madhusudan and Daniel Neider},
We introduce a robust learning paradigm for synthesizing invariants, called ICE-learning, that learns using examples, counter-examples, and implications, and show that it admits honest teachers and convergent mechanisms for invariant synthesis. We observe that existing algorithms for black-box abstract interpretation can be interpreted as ICE learning algorithms. We develop new convergent ICE-learning algorithms for two domains, one for learning Boolean combinations of numerical invariants for… CONTINUE READING
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