Abstract
Compositional reasoning aims to improve scalability of verification tools by reducing the original verification task into subproblems. The simplification is typically based on the assume-guarantee reasoning principles, and requires decomposing the system into components as well as identifying adequate environment assumptions for components. One recent approach to automatic derivation of adequate assumptions is based on the L * algorithm for active learning of regular languages. In this paper, we present a fully automatic approach to compositional reasoning by automating the decomposition step using an algorithm for hypergraph partitioning for balanced clustering of variables. We also propose heuristic improvements to the assumption identification phase. We report on an implementation based on NuSMV, and experiments that study the effectiveness of automatic decomposition and the overall savings in the computational requirements of symbolic model checking.
This research was partially supported by ARO grant DAAD19-01-1-0473, and NSF grants ITR/SY 0121431 and CCR0306382.
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References
Abadi, M., Lamport, L.: Conjoining specifications. ACM TOPLAS 17, 507–534 (1995)
Alur, R., Henzinger, T.A.: Reactive modules. Formal Methods in System Design 15(1), 7–48 (1996); A preliminary version appears in Proc. 11th LICS, 1996
Alur, R., Madhusudan, P., Nam, W.: Symbolic compositional verification by learning assumptions. In: Etessami, K., Rajamani, S.K. (eds.) CAV 2005. LNCS, vol. 3576, pp. 548–562. Springer, Heidelberg (2005)
Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75, 87–106 (1987)
Barringer, H., Pasareanu, C.S., Giannakopolou, D.: Proof rules for automated compositional verification through learning. In: Proc. 2nd SVCBS (2003)
Bryant, R.E.: Graph-based algorithms for boolean-function manipulation. IEEE Transactions on Computers C-35(8), 677–691 (1986)
Cimatti, A., Clarke, E., Giunchiglia, E., Giunchiglia, F., Pistore, M., Roveri, M., Sebastiani, R., Tacchella, A.: NuSMV Version 2: An OpenSource Tool for Symbolic Model Checking. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, pp. 359–364. Springer, Heidelberg (2002)
Cobleigh, J.M., Avrunin, G.S., Clarke, L.A.: Breaking up is hard to do: an investigation of decomposition for assume-guarantee reasoning. Technical Report UM-CS-2004-023 (2005)
Cobleigh, J.M., Giannakopoulou, D., Pasareanu, C.S.: Learning assumptions for compositional verification. In: Garavel, H., Hatcliff, J. (eds.) ETAPS 2003 and TACAS 2003. LNCS, vol. 2619, pp. 331–346. Springer, Heidelberg (2003)
Fiduccia, C.M., Mattheyses, R.M.: A linear time heuristic for improving network partitions. In: Proc. of 19th DAC, pp. 175–181 (1982)
Giannakopoulou, D., Pasareanu, C.S.: Learning-based assume-guarantee verification. In: Godefroid, P. (ed.) SPIN 2005. LNCS, vol. 3639, pp. 282–287. Springer, Heidelberg (2005)
Grümberg, O., Long, D.E.: Model checking and modular verification. ACM Transactions on Programming Languages and Systems 16(3), 843–871 (1994)
Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Trans. VLSI Systems 7(1), 69–79 (1999)
Karypis, G., Kumar, V.: Multilevel k-way hypergraph partitioning. In: Proc. of 36th Design Automation Conference, pp. 343–348 (1999)
Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. The Bell System Technical Journal 49(2), 291–307 (1970)
McMillan, K.L.: A compositional rule for hardware design refinement. In: Grumberg, O. (ed.) CAV 1997. LNCS, vol. 1254, pp. 24–35. Springer, Heidelberg (1997)
Rivest, R.L., Schapire, R.E.: Inference of finite automata using homing sequences. Information and Computation 103(2), 299–347 (1993)
Sharygina, N., Chaki, S., Clarke, E.M., Sinha, N.: Dynamic component substitutability analysis. In: Fitzgerald, J.S., Hayes, I.J., Tarlecki, A. (eds.) FM 2005. LNCS, vol. 3582, pp. 512–528. Springer, Heidelberg (2005)
Stark, E.W.: A proof technique for rely-guarantee properties. In: Maheshwari, S.N. (ed.) FSTTCS 1985. LNCS, vol. 206, pp. 369–391. Springer, Heidelberg (1985)
Weiser, M.: Program slicing. IEEE Trans. on Software Engineering 10, 352–357 (1984)
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Nam, W., Alur, R. (2006). Learning-Based Symbolic Assume-Guarantee Reasoning with Automatic Decomposition. In: Graf, S., Zhang, W. (eds) Automated Technology for Verification and Analysis. ATVA 2006. Lecture Notes in Computer Science, vol 4218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11901914_15
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DOI: https://doi.org/10.1007/11901914_15
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