A Fast Counterexample Minimization Approach with Refutation Analysis and Incremental SAT

Shen, ShengYu, Qin, Ying and Li, SiKun A Fast Counterexample Minimization Approach with Refutation Analysis and Incremental SAT., 2005 . In 10th Asia and South Pacific Design Automation Conference, ShangHai (China), January 2005. [Conference paper]

Preview

PDF ASPDAC05_5C-4s.pdf Download (657kB) | Preview

English abstract

It is a hotly research topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. BFL algorithm is the most effective Counterexample minimization algorithm compared to all other approaches, but its run time overhead is very large due to one call to SAT solver per candidate variable to be eliminated. So we propose a faster counterexample minimization algorithm based on refutation analysis and incremental SAT. First, for every UNSAT instance of BFL, we perform refutation analysis to extract the set of variables that lead to UNSAT, all variables not belong to this set can be eliminated simultaneously. In this way, we can eliminate many variables with only one call to SAT solver. At the same time, we employ incremental SAT approach to share learned clauses between similar instances of BFL, to prevent overlapped state space from being searched repeatedly. Theoretic analysis and experiment result shows that, our approach can be 1 to 2 orders of magnitude faster than BFL, and still retain the minimization ability of BFL.

Item type:	Conference paper
Keywords:	Model Checking Counterexample Minimization
Subjects:	L. Information technology and library technology > LJ. Software. L. Information technology and library technology > LK. Software methodologies and engineering.
Depositing user:	ShengYu Shen
Date deposited:	01 Aug 2006
Last modified:	02 Oct 2014 12:04
URI:	http://hdl.handle.net/10760/7864

References

K Ravi and Fabio Somenzi. Minimal Assignments for Bounded Model Checking. In TACAS'04,pages 31-45,2004. LNCS 2988.

H.Jin, K.Ravi,and F.Somenzi. "Fate and free will in error traces".TACAS’02,pp 445-458,2002.LNCS 2280.

A. Biere, A. Cimatti, E.M. Clarke, M. Fujita, Y. Zhu ."Symbolic Model Checking using SAT procedures instead of BDDs".In DAC’99.pages 317-320, 1999.

A. Cimatti, et.al "NuSMV 2: An OpenSource Tool for Symbolic Model Checking". In CAV’02,pages 359-364, 2002.LNCS 2404

M. Moskewicz, C. F. Madigan, Y. Zhao, L. Zhang, and S. Malik. Chaff: Engineering an efficient SAT solver. In DAC’01, pages 530-535,2001.

http://www.cbl.ncsu.edu/CBL_Docs/iscas89.html

N. Een and N. Sorensson. Temporal Induction by Incremental SAT Solving. In BMC’03.

L.Zhang, C.Madigan, M.Moskewicz, and S.Malik. Efficient conflict driven learning in a Boolean satisfiability solver. ICCAD 2001.

Downloads

Actions (login required)

View Item