Learning-Based Assume-Guarantee Regression Verification

  • Fei HeEmail author
  • Shu Mao
  • Bow-Yaw Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9779)


Due to enormous resource consumption, model checking each revision of evolving systems repeatedly is impractical. To reduce cost in checking every revision, contextual assumptions are reused from assume-guarantee reasoning. However, contextual assumptions are not always reusable. We propose a fine-grained learning technique to maximize the reuse of contextual assumptions. Based on fine-grained learning, we develop a regressional assume-guarantee verification approach for evolving systems. We have implemented a prototype of our approach and conducted extensive experiments (with 1018 verification tasks). The results suggest promising outlooks for our incremental technique.


Model Check Symbolic Representation Logical Formula Verification Task Proof Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Backes, J., Person, S., Rungta, N., Tkachuk, O.: Regression verification using impact summaries. In: Bartocci, E., Ramakrishnan, C.R. (eds.) SPIN 2013. LNCS, vol. 7976, pp. 99–116. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Baier, C., Katoen, J.P.: Principles of Model Checking. MIT Press, Cambridge (2008)zbMATHGoogle Scholar
  5. 5.
    Berry, M.: Proving properties of the lift system. Master’s thesis, School of Computer Science, University of Birmingham, vol. 199, issue 6 (1996)Google Scholar
  6. 6.
    Beyer, D., Löwe, S., Novikov, E., Stahlbauer, A., Wendler, P.: Precision reuse for efficient regression verification. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, pp. 389–399. ACM (2013)Google Scholar
  7. 7.
    Beyer, D., Wendler, P.: Reuse of verification results. In: Bartocci, E., Ramakrishnan, C.R. (eds.) SPIN 2013. LNCS, vol. 7976, pp. 1–17. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Bshouty, N.H.: Exact learning Boolean function via the monotone theory. Inf. Comput. 123(1), 146–153 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Chaki, S., Clarke, E., Sharygina, N., Sinha, N.: Verification of evolving software via component substitutability analysis. Formal Methods Syst. Des. 32(3), 235–266 (2008)CrossRefzbMATHGoogle Scholar
  10. 10.
    Chaki, S., Gurfinkel, A., Strichman, O.: Regression verification for multi-threaded programs. In: Kuncak, V., Rybalchenko, A. (eds.) VMCAI 2012. LNCS, vol. 7148, pp. 119–135. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Chaki, S., Strichman, O.: Optimized L*-based assume-guarantee reasoning. In: Grumberg, O., Huth, M. (eds.) TACAS 2007. LNCS, vol. 4424, pp. 276–291. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Chaum, D.: The dining cryptographers problem: unconditional sender and recipient untraceability. J. Cryptol. 1(1), 65–75 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Chen, Y.-F., Clarke, E.M., Farzan, A., He, F., Tsai, M.-H., Tsay, Y.-K., Wang, B.-Y., Zhu, L.: Comparing learning algorithms in automated assume-guarantee reasoning. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010, Part I. LNCS, vol. 6415, pp. 643–657. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Chen, Y.-F., Clarke, E.M., Farzan, A., Tsai, M.-H., Tsay, Y.-K., Wang, B.-Y.: Automated assume-guarantee reasoning through implicit learning. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 511–526. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Chen, Y.-F., Farzan, A., Clarke, E.M., Tsay, Y.-K., Wang, B.-Y.: Learning minimal separating DFA’s for compositional verification. In: Kowalewski, S., Philippou, A. (eds.) TACAS 2009. LNCS, vol. 5505, pp. 31–45. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Cimatti, A., Clarke, E., Giunchiglia, E., Giunchiglia, F., Pistore, M., Roveri, M., Sebastiani, R., Tacchella, A.: NuSMV 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)CrossRefGoogle Scholar
  17. 17.
    Clarke, E.M., Grumberg, O., Peled, D.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Cobleigh, J.M., Giannakopoulou, D., Păsăreanu, C.S.: Learning assumptions for compositional verification. In: Garavel, H., Hatcliff, J. (eds.) TACAS 2003. LNCS, vol. 2619, pp. 331–346. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  19. 19.
    Gavaldà, R., Guijarro, D.: Learning ordered binary decision diagrams. In: Zeugmann, T., Shinohara, T., Jantke, K.P. (eds.) ALT 1995. LNCS, vol. 997, pp. 228–238. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  20. 20.
    He, F., Gao, X., Wang, B.Y., Zhang, L.: Leveraging weighted automata in compositional reasoning about concurrent probabilistic systems. In: Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, pp. 503–514. ACM (2015)Google Scholar
  21. 21.
    He, F., Wang, B.Y., Yin, L., Zhu, L.: Symbolic assume-guarantee reasoning through BDD learning. In: ICSE, pp. 1071–1082. ACM (2014)Google Scholar
  22. 22.
    Lauterburg, S., Sobeih, A., Marinov, D., Viswanathan, M.: Incremental state-space exploration for programs with dynamically allocated data. In: Proceedings of the 30th International Conference on Software Engineering, pp. 291–300. ACM (2008)Google Scholar
  23. 23.
    Nakamura, A.: An efficient query learning algorithm for ordered binary decision diagrams. Inf. Comput. 201(2), 178–198 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Plath, M., Ryan, M.: Feature integration using a feature construct. Sci. Comput. Program. 41(1), 53–84 (2001)CrossRefzbMATHGoogle Scholar
  25. 25.
    Sery, O., Fedyukovich, G., Sharygina, N.: Incremental upgrade checking by means of interpolation-based function summaries. In: Formal Methods in Computer-Aided Design (FMCAD), pp. 114–121. IEEE (2012)Google Scholar
  26. 26.
    Sharygina, N., Chaki, S., Clarke, E., 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)CrossRefGoogle Scholar
  27. 27.
    Strichman, O., Godlin, B.: Regression verification - a practical way to verify programs. In: Meyer, B., Woodcock, J. (eds.) VSTTE 2005. LNCS, vol. 4171, pp. 496–501. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  28. 28.
    Visser, W., Geldenhuys, J., Dwyer, M.B.: Green: reducing, reusing and recycling constraints in program analysis. In: Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering, p. 58. ACM (2012)Google Scholar
  29. 29.
    Yang, G., Dwyer, M.B., Rothermel, G.: Regression model checking. In: IEEE International Conference on Software Maintenance, ICSM 2009, pp. 115–124. IEEE (2009)Google Scholar
  30. 30.
    Zhu, H., He, F., Hung, W.N., Song, X., Gu, M.: Data mining based decomposition for assume-guarantee reasoning. In: FMCAD, pp. 116–119. IEEE (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tsinghua National Laboratory for Information Science and Technology (TNList)BeijingChina
  2. 2.School of SoftwareTsinghua UniversityBeijingChina
  3. 3.Key Laboratory for Information System Security, Ministry of EducationBeijingChina
  4. 4.Academia SinicaTaipeiTaiwan

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