Predicting Lemmas in Generalization of IC3

TL;DR

This paper introduces a novel method to predict minimal lemmas in the IC3 model checking algorithm using counterexample to propagation, significantly enhancing efficiency and reducing computation time.

Contribution

The paper presents a new approach to predict minimal lemmas before variable dropping in IC3, improving performance and efficiency in safety model checking.

Findings

High success rate in lemma prediction

Significant performance improvements in IC3

Reduced time spent on inductive generalization

Abstract

The IC3 algorithm, also known as PDR, has made a significant impact in the field of safety model checking in recent years due to its high efficiency, scalability, and completeness. The most crucial component of IC3 is inductive generalization, which involves dropping variables one by one and is often the most time-consuming step. In this paper, we propose a novel approach to predict a possible minimal lemma before dropping variables by utilizing the counterexample to propagation (CTP). By leveraging this approach, we can avoid dropping variables if predict successfully. The comprehensive evaluation demonstrates a commendable success rate in lemma prediction and a significant performance improvement achieved by our proposed method.