Advertisement

Symbolic Learning of Component Interfaces

  • Dimitra Giannakopoulou
  • Zvonimir Rakamarić
  • Vishwanath Raman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7460)

Abstract

Given a white-box component Open image in new window with specified unsafe states, we address the problem of automatically generating an interface that captures safe orderings of invocations of Open image in new window ’s public methods. Method calls in the generated interface are guarded by constraints on their parameters. Unlike previous work, these constraints are generated automatically through an iterative refinement process. Our technique, named Psyco (Predicate-based SYmbolic COmpositional reasoning), employs a novel combination of the L* automata learning algorithm with symbolic execution. The generated interfaces are three-valued, capturing whether a sequence of method invocations is safe, unsafe, or its effect on the component state is unresolved by the symbolic execution engine. We have implemented Psyco as a new prototype tool in the JPF open-source software model checking platform, and we have successfully applied it to several examples.

Keywords

Label Transition System Symbolic Execution Automaton Learn Method Signature Component Interface 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aarts, F., Jonsson, B., Uijen, J.: Generating models of infinite-state communication protocols using regular inference with abstraction. In: ICTSS, pp. 188–204 (2010)Google Scholar
  2. 2.
    Alur, R., Cerný, P., Madhusudan, P., Nam, W.: Synthesis of interface specifications for Java classes. In: POPL, pp. 98–109 (2005)Google Scholar
  3. 3.
    Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Burckhardt, S., Dern, C., Musuvathi, M., Tan, R.: Line-up: A complete and automatic linearizability checker. In: PLDI, pp. 330–340 (2010)Google Scholar
  5. 5.
    Chaki, S., Strichman, O.: Three optimizations for assume-guarantee reasoning with L*. FMSD 32(3), 267–284 (2008)zbMATHGoogle Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    Cho, C.Y., Babić, D., Poosankam, P., Chen, K.Z., Wu, E.X., Song, D.: MACE: Model-inference-assisted concolic exploration for protocol and vulnerability discovery. In: USENIX Security Symposium (2011)Google Scholar
  9. 9.
    Dutertre, B., Moura, L.D.: The Yices SMT solver. Technical report, SRI International (2006)Google Scholar
  10. 10.
    Gheorghiu, M., Giannakopoulou, D., Păsăreanu, C.S.: Refining Interface Alphabets for Compositional Verification. In: Grumberg, O., Huth, M. (eds.) TACAS 2007. LNCS, vol. 4424, pp. 292–307. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Giannakopoulou, D., Păsăreanu, C.S.: Interface Generation and Compositional Verification in JavaPathfinder. In: Chechik, M., Wirsing, M. (eds.) FASE 2009. LNCS, vol. 5503, pp. 94–108. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Giannakopoulou, D., Rakamarić, Z., Raman, V.: Symbolic learning of component interfaces. Technical report, NASA Ames Research Center (2012)Google Scholar
  13. 13.
    Godefroid, P., Klarlund, N., Sen, K.: DART: Directed automated random testing. SIGPLAN Not. 40(6), 213–223 (2005)CrossRefGoogle Scholar
  14. 14.
    Guava: Google core libraries, http://code.google.com/p/guava-libraries/
  15. 15.
    Gupta, A., McMillan, K.L., Fu, Z.: Automated Assumption Generation for Compositional Verification. In: Damm, W., Hermanns, H. (eds.) CAV 2007. LNCS, vol. 4590, pp. 420–432. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Henzinger, T.A., Jhala, R., Majumdar, R.: Permissive interfaces. In: ESEC/FSE, pp. 31–40 (2005)Google Scholar
  17. 17.
    Howar, F., Steffen, B., Jonsson, B., Cassel, S.: Inferring Canonical Register Automata. In: Kuncak, V., Rybalchenko, A. (eds.) VMCAI 2012. LNCS, vol. 7148, pp. 251–266. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Howar, F., Steffen, B., Merten, M.: Automata Learning with Automated Alphabet Abstraction Refinement. In: Jhala, R., Schmidt, D. (eds.) VMCAI 2011. LNCS, vol. 6538, pp. 263–277. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Joshi, S., Lahiri, S.K., Lal, A.: Underspecified harnesses and interleaved bugs. In: POPL, pp. 19–30 (2012)Google Scholar
  20. 20.
  21. 21.
    King, J.C.: Symbolic execution and program testing. Commun. ACM 19(7), 385–394 (1976)zbMATHCrossRefGoogle Scholar
  22. 22.
    Pasareanu, C.S., Giannakopoulou, D., Bobaru, M.G., Cobleigh, J.M., Barringer, H.: Learning to divide and conquer: applying the L* algorithm to automate assume-guarantee reasoning. FMSD 32(3), 175–205 (2008)zbMATHGoogle Scholar
  23. 23.
    Rivest, R.L., Schapire, R.E.: Inference of finite automata using homing sequences. Inf. Comput. 103(2), 299–347 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Sen, K., Marinov, D., Agha, G.: CUTE: A concolic unit testing engine for C. In: ESEC/FSE, pp. 263–272 (2005)Google Scholar
  25. 25.
    Singh, R., Giannakopoulou, D., Păsăreanu, C.: Learning Component Interfaces with May and Must Abstractions. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 527–542. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dimitra Giannakopoulou
    • 1
  • Zvonimir Rakamarić
    • 2
  • Vishwanath Raman
    • 3
  1. 1.NASA Ames Research CenterUSA
  2. 2.School of ComputingUniversity of UtahUSA
  3. 3.Carnegie Mellon UniversityUSA

Personalised recommendations