PranCS: A Protocol and Discrete Controller Synthesis Tool

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10606)

Abstract

PranCS is a tool for synthesizing protocol adapters and discrete controllers. It exploits general search techniques such as simulated annealing and genetic programming for homing in on correct solutions, and evaluates the fitness of candidates by using model-checking results. Our Proctocol and Controller Synthesis (PranCS) tool uses NuSMV as a back-end for the individual model-checking tasks and a simple candidate mutator to drive the search.

PranCS is also designed to explore the parameter space of the search techniques it implements. In this paper, we use PranCS to study the influence of turning various parameters in the synthesis process.

This is a preview of subscription content, log in to check access.

References

  1. 1.
    Altisen, K., Clodic, A., Maraninchi, F., Rutten, E.: Using controller-synthesis techniques to build property-enforcing layers. In: Degano, P. (ed.) ESOP 2003. LNCS, vol. 2618, pp. 174–188. Springer, Heidelberg (2003). doi: 10.1007/3-540-36575-3_13 CrossRefGoogle Scholar
  2. 2.
    Asarin, E., Maler, O., Pnueli, A.: Symbolic controller synthesis for discrete and timed systems. In: Antsaklis, P., Kohn, W., Nerode, A., Sastry, S. (eds.) HS 1994. LNCS, vol. 999, pp. 1–20. Springer, Heidelberg (1995). doi: 10.1007/3-540-60472-3_1 CrossRefGoogle Scholar
  3. 3.
    Berthier, N., Maraninchi, F., Mounier, L.: Synchronous Programming of Device Drivers for Global Resource Control in Embedded Operating Systems. ACM Trans. Embed. Comput. Syst. 12(1s), 39: 1–39: 26., March 2013Google Scholar
  4. 4.
    Berthier, N., Marchand, H.: Discrete controller synthesis for infinite state systems with ReaX. In: 12th Internation Workshop on Discrete Event Systems. WODES 20114, IFAC, pp. 46–53, May 2014Google Scholar
  5. 5.
    Burch, J.R., Clarke, E.M., McMillan, K.L., Dill, D.L., Hwang, L.J.: Symbolic model checking: \(10^{20}\) states and beyond. Inf. Comput. 98(2), 142–170 (1992)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    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). doi: 10.1007/3-540-45657-0_29 CrossRefGoogle Scholar
  7. 7.
    Clark, J.A., Jacob, J.L.: Protocols are programs too: the meta-heuristic search for security protocols. Inf. Softw. Technol. 43, 891–904 (2001)CrossRefGoogle Scholar
  8. 8.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
  9. 9.
    Connolly, D.: An improved annealing scheme for the qap. Eur. J. Oper. Res. 46, 93–100 (1990)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Cury, J.E., Krogh, B.H., Niinomi, T.: Synthesis of supervisory controllers for hybrid systems based on approximating automata. IEEE Trans. Autom. Control 43(4), 564–568 (1998)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Girault, A., Rutten, É.: Automating the addition of fault tolerance with discrete controller synthesis. Formal Methods Syst. Des. 35(2), 190 (2009)CrossRefMATHGoogle Scholar
  12. 12.
    Henderson, D., Jacobson, S.H., Johnson, A.W.: The theory and practice of simulated annealing. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, International Series in Operations Research & Management Science, vol. 57, pp. 287–319. Springer, Boston (2003). doi: 10.1007/0-306-48056-5_10 Google Scholar
  13. 13.
    Husien, I., Berthier, N., Schewe, S.: A hot method for synthesising cool controllers. In: Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software. SPIN 2017, pp. 122–131. ACM, New York (2017)Google Scholar
  14. 14.
    Husien, I., Schewe, S.: Program generation using simulated annealing and model checking. In: De Nicola, R., Kühn, E. (eds.) SEFM 2016. LNCS, vol. 9763, pp. 155–171. Springer, Cham (2016). doi: 10.1007/978-3-319-41591-8_11 Google Scholar
  15. 15.
    Johnson, C.G.: Genetic programming with fitness based on model checking. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 114–124. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71605-1_11 CrossRefGoogle Scholar
  16. 16.
    Katz, G., Peled, D.: Model checking-based genetic programming with an application to mutual exclusion. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 141–156. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-78800-3_11 CrossRefGoogle Scholar
  17. 17.
    Katz, G., Peled, D.: Model checking driven heuristic search for correct programs. In: Peled, D.A., Wooldridge, M.J. (eds.) MoChArt 2008. LNCS (LNAI), vol. 5348, pp. 122–131. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-00431-5_8 CrossRefGoogle Scholar
  18. 18.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  19. 19.
    Krogh, B.H., Holloway, L.E.: Synthesis of feedback control logic for discrete manufacturing systems. Automatica 27(4), 641–651 (1991)CrossRefGoogle Scholar
  20. 20.
    Marchand, H., Bournai, P., Le Borgne, M., Le Guernic, P.: Synthesis of discrete-event controllers based on the signal environment. Discrete Event Dynamic Syst. Theory Appl. 10(4), 325–346 (2000)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Pnueli, A., Rosner, R.: On the synthesis of a reactive module. In: Proceedings of the 16th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. POPL 1989. pp. 179–190. ACM, New York (1989)Google Scholar
  22. 22.
    Ramadge, P., Wonham, W.: The control of discrete event systems. Proc. IEEE Spec. Issue Dyn. Discr. Event Syst. 77(1), 81–98 (1989)MATHGoogle Scholar
  23. 23.
    Wang, Y., Lafortune, S., Kelly, T., Kudlur, M., Mahlke, S.: The theory of deadlock avoidance via discrete control. In: Proceedings of the 36th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, pp. 252–263. POPL 2009. ACM, New York (2009)Google Scholar
  24. 24.
    Zhou, M., DiCesare, F.: Petri Net Synthesis for Discrete Event Control of Manufacturing Systems, vol. 204. Springer Science & Business Media, Heidelberg (2012). doi: 10.1007/978-1-4615-3126-5 MATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK

Personalised recommendations