CLN2INV: Learning Loop Invariants with Continuous Logic Networks

Abstract

Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops. In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces. Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT) for loop invariants from program execution traces. We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently. We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset. CLN2INV is the first tool to solve all 124 theoretically solvable problems in the Code2Inv dataset. Moreover, CLN2INV takes only 1.1 seconds on average for each problem, which is 40× faster than existing approaches. We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants than the ones required for the Code2Inv dataset. * Co-student leads listed in alphabetical order; each contributed equally.

Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis

“…We provide examples of Code2Inv problems in Appendix C. Most existing invariant generation tools can only solve a subset of these problems [28]. To the best of our knowledge, only one existing tool can solve them all [29], but only by using specific optimization techniques that are not generalizable beyond the setting of purely numerical programs.…”

Vico

“…Loop Invariants: Finding loop invariants is one of the fundamental problems in program verification. Numerous works [15,16,41,42,61,79,81,93] have been proposed over the decades for detecting loop invariants, inferring them and then utilizing their knowledge for program verification. In contrast to the prior works, we explored the problem of detecting loop-invariants with the view of specific loop optimizations.…”