Xiaoyu Zhang, Shengping Xiao, Jianwen Li, G. Pu, O. Strichman
{"title":"Combining BMC and Complementary Approximate Reachability to Accelerate Bug-Finding","authors":"Xiaoyu Zhang, Shengping Xiao, Jianwen Li, G. Pu, O. Strichman","doi":"10.1145/3508352.3549393","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":270592,"journal":{"name":"2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","volume":"21 17","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3508352.3549393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.