Abstract
We describe a method for computing a minimum-state automaton to act as an intermediate assertion in assume-guarantee reasoning, using a sampling approach and a Boolean satisfiability solver. For a set of synthetic benchmarks intended to mimic common situations in hardware verification, this is shown to be significantly more effective than earlier approximate methods based on Angluin’s L* algorithm. For many of these benchmarks, this method also outperforms BDD-based model checking and interpolation-based model checking. We also demonstrate how domain knowledge can be incorporated into our algorithm to improve its performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alur R, Madhusudan P, Nam W (2005) Symbolic compositional verification by learning assumptions. In: Proceedings of the international conference on computer aided verification (CAV), pp 548–562
Angluin D (1987) Learning regular sets from queries and counterexamples. Inf Comput 75:87–106
Biere A, Cimatti A, Clarke E, Zhu Y (1999) Symbolic model checking without BDDs. In: Tools and algorithms for the construction and analysis of systems (TACAS’99), LNCS
Cobleigh J, Giannakopoulou D, Pasareanu C (2003) Learning assumptions for compositional verification. In: Proceedings of the 9th international conference on tools and algorithms for the construction and analysis of systems (TACAS)
Chaki S, Strichman O (2007) Optimized l*-based assume-guarantee reasoning. In: TACAS, pp 276–291
Gold EM (1978) Complexity of automaton identification from given data. Inf Comput 37:302–320
Kam T, Villa T, Brayton R, Sangiovanni-Vincentelli AL (1997) Synthesis of FSMs: functional optimization. Kluwer Academic, Dordrecht
McMillan KL Cadence SMV. Cadence Berkeley Labs, CA
McMillan KL (1993) Symbolic model checking. Kluwer Academic, Boston
Mitchell TM (1997) Machine learning. WCB/McGraw-Hill, New York
Oliveira AL, Marques Silva JP (1998) Efficient search techniques for the inference of minimum size finite automata. In: Proceedings of the symposium on string processing and information retrieval (SPIRE), pp 81–89
Pena JM, Oliveira AL (1999) A new algorithm for exact reduction of incompletely specified finite state machines. IEEE Trans CAD Integr Circuits Syst 18(11):1619–1632
Pfleeger CF (1973) State reduction in incompletely specified finite state machines. IEEE Trans Comput C-22:1099–1102
Quinlan JR (1986) Induction of decision trees. Mach Learn
Rivest RL, Schapire RE (1989) Inference of finite automata using homing sequences. In: Proceedings of the ACM symposium on theory of computing (STOC). ACM Press, New York, pp 411–420
Sinha N, Clarke EM (2007) Sat-based compositional verification using lazy learning. In: CAV, pp 39–54
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gupta, A., McMillan, K.L. & Fu, Z. Automated assumption generation for compositional verification. Form Methods Syst Des 32, 285–301 (2008). https://doi.org/10.1007/s10703-008-0050-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10703-008-0050-0