DIG is a numerical invariant generation tool. It infers program invariants or properties over (i) program execution traces or (ii) program source code. DIG supports many forms of numerical invariants, including nonlinear equalities, octagonal and interval properties, min/max-plus relations, and congruence relations.
machine-learning symbolic-execution dynamic-analysis program-verification loop-invariants invariant-generation specification-mining neural-network-verification
-
Updated
Sep 5, 2024 - Python