  Automated synthesis of inductive invariants is an important problem in
software verification. Once all the invariants have been specified, software
verification reduces to checking of verification conditions. Although static
analyses to infer invariants have been studied for over forty years, recent
years have seen a flurry of data-driven invariant inference techniques which
guess invariants from examples instead of analyzing program text. However,
these techniques have been demonstrated to scale only to programs with a small
number of variables. In this paper, we study these scalability issues and
address them in our tool oasis that improves the scale of data-driven invariant
inference and outperforms state-of-the-art systems on benchmarks from the
invariant inference track of the Syntax Guided Synthesis competition.
