Optimizing automatic abstraction refinement for generalized symbolic trajectory evaluation
Yan Chen, Fei Xie, Jin Seok Yang · 2008
In this paper, we present a suite of optimizations targeting automatic abstraction refinement for Generalized Symbolic Trajectory Evaluation (GSTE). We optimize both model refinement and spec refinement supported by AutoGSTE: a counterexample-guided refinement loop for GSTE. Experiments on a family of benchmark circuits have shown that our optimizations lead to major efficiency improvements in verification involving abstraction refinement.