Learning invariants using decision trees and implication counterexamples

  title={Learning invariants using decision trees and implication counterexamples},
  author={Pranav Garg and Daniel Neider and P. Madhusudan and Dan Roth},
Inductive invariants can be robustly synthesized using a learning model where the teacher is a program verifier who instructs the learner through concrete program configurations, classified as positive, negative, and implications. We propose the first learning algorithms in this model with implication counter-examples that are based on machine learning techniques. In particular, we extend classical decision-tree learning algorithms in machine learning to handle implication samples, building new… CONTINUE READING
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