Section: New Results

SAT based bounded model checking

Participants : Mingsong Chen, Haifeng Gu, Xinqian Zhang.

SAT-based Bounded Model Checking (BMC) is promising for automated generation of directed tests. Due to the state space explosion problem, SAT-based BMC is unsuitable to handle complex properties with large SAT instances or large bounds. In this work, we propose a framework to automatically scale down the SAT falsification complexity by utilizing the decision ordering based learning from decomposed sub-properties. Our framework makes three important contributions: i) it proposes learning-oriented decomposition techniques for complex property falsification, ii) it proposes an efficient approach to accelerate the complex property falsification using the learning from decomposed sub-properties, and iii) it combines the advantages of both property decomposition and property clustering to reduce the overall test generation time. The experimental results [11] using both software and hardware benchmarks demonstrate the effectiveness of our framework.