CLN2INV: Learning Loop Invariants with Continuous Logic Networks

TL;DR

This paper introduces CLN2INV, a neural approach that automatically learns precise loop invariants from program traces, significantly improving speed and accuracy over existing methods in program verification.

Contribution

The paper presents Continuous Logic Networks (CLNs) and their application in CLN2INV, the first tool to solve all solvable problems in Code2Inv with faster performance and ability to learn more complex invariants.

Findings

CLN2INV outperforms existing approaches on Code2Inv dataset.

CLN2INV solves all 124 solvable problems in Code2Inv.

Average solving time is 1.1 seconds, 40 times faster than previous methods.

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 times 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.