Finding Kernels in Non-Linear Data-Driven CHC Solving

Abstract

Program verification has seen a lot of progress, but its still unable to automatically find proofs for industry programs. This paper builds on data-driven approaches from previous work [11] to provide a more robust automatic prover for programs with non-linear loop invariants. It does so by attempting to find the correct kernel for the relation that makes the invariant linear. This is an easy addition to existing systems and can be used with any data-driven approach, allowing it to be easily implemented on top of them. By finding a suitable kernel, many difficult non-linear invariants are easily found.