TY - GEN
T1 - Combining BMC and Complementary Approximate Reachability to Accelerate Bug-Finding
AU - Zhang, Xiaoyu
AU - Xiao, Shengping
AU - Li, Jianwen
AU - Pu, Geguang
AU - Strichman, Ofer
N1 - Publisher Copyright:
© 2022 Association for Computing Machinery.
PY - 2022/10/30
Y1 - 2022/10/30
N2 - Bounded Model Checking (BMC) is so far considered as the best engine for bug-finding in hardware model checking. Given a bound K, BMC can detect if there is a counterexample to a given temporal property within K steps from the initial state, thus performing a global-style search. Recently, a SAT-based model-checking technique called Complementary Approximate Reachability (CAR) was shown to be complementary to BMC, in the sense that frequently they can solve instances that the other technique cannot, within the same time limit. CAR detects a counterexample gradually with the guidance of an over-approximating state sequence, and performs a local-style search. In this paper, we consider three different ways to combine BMC and CAR. Our experiments show that they all outperform BMC and CAR on their own, and solve instances that cannot be solved by these two techniques. Our findings are based on a comprehensive experimental evaluation using the benchmarks of two hardware model checking competitions.
AB - Bounded Model Checking (BMC) is so far considered as the best engine for bug-finding in hardware model checking. Given a bound K, BMC can detect if there is a counterexample to a given temporal property within K steps from the initial state, thus performing a global-style search. Recently, a SAT-based model-checking technique called Complementary Approximate Reachability (CAR) was shown to be complementary to BMC, in the sense that frequently they can solve instances that the other technique cannot, within the same time limit. CAR detects a counterexample gradually with the guidance of an over-approximating state sequence, and performs a local-style search. In this paper, we consider three different ways to combine BMC and CAR. Our experiments show that they all outperform BMC and CAR on their own, and solve instances that cannot be solved by these two techniques. Our findings are based on a comprehensive experimental evaluation using the benchmarks of two hardware model checking competitions.
UR - http://www.scopus.com/inward/record.url?scp=85145650024&partnerID=8YFLogxK
U2 - 10.1145/3508352.3549393
DO - 10.1145/3508352.3549393
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85145650024
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
T2 - 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
Y2 - 30 October 2022 through 4 November 2022
ER -