Advertisement

Learning Moore machines from input–output traces

  • Georgios GiantamidisEmail author
  • Stavros Tripakis
  • Stylianos Basagiannis
STTT Regular Paper
  • 6 Downloads

Abstract

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper, we study this problem for finite-state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input–output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. We also carry out a performance comparison against two existing tools (LearnLib and flexfringe). Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample.

Keywords

Finite state machine Moore machine Mealy machine Automata learning Passive learning Characteristic sample 

Notes

References

  1. 1.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  2. 2.
    Vaandrager, F.: Model learning. Commun. ACM 60(2), 86–95 (2017)CrossRefGoogle Scholar
  3. 3.
    Tripakis, S.: Data-driven and model-based design. In: 1st IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) (2018)Google Scholar
  4. 4.
    Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  5. 5.
    Solar-Lezama, A.: Program sketching. STTT 15(5–6), 475–495 (2013)CrossRefGoogle Scholar
  6. 6.
    Gulwani, S.: Automating string processing in spreadsheets using input-output examples. In: 38th POPL, pp. 317–330 (2011)Google Scholar
  7. 7.
    Seshia, S.A.: Sciduction: combining induction, deduction, and structure for verification and synthesis. In: DAC, pp. 356–365 (2012)Google Scholar
  8. 8.
    Ray, B., Posnett, D., Filkov, V., Devanbu, P.: A large scale study of programming languages and code quality in github. In: ACM SIGSOFT, FSE’14 (2014)Google Scholar
  9. 9.
    Alur, R., Martin, M., Raghothaman, M., Stergiou, C., Tripakis, S., Udupa, A.: Synthesizing finite-state protocols from scenarios and requirements. In: HVC, Volume 8855 of LNCS. Springer (2014)Google Scholar
  10. 10.
    Alur, R., Tripakis, S.: Automatic synthesis of distributed protocols. SIGACT News 48(1), 55–90 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zeller, A.: Why Programs Fail—A Guide to Systematic Debugging, 2nd edn. Academic Press, Cambridge (2009)Google Scholar
  12. 12.
    Kohavi, Z.: Switching and Finite Automata Theory, 2nd edn. McGraw-Hill, New York (1978)zbMATHGoogle Scholar
  13. 13.
    de la Higuera, C.: Grammatical Inference: Learning Automata and Grammars. CUP, Cambridge (2010)CrossRefGoogle Scholar
  14. 14.
    Gold, E.M.: Language identification in the limit. Inf. Control 10(5), 447–474 (1967)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Raffelt, H., Steffen, B.: Learnlib: a library for automata learning and experimentation, vol. 3922, pp. 377–380 (2006)Google Scholar
  16. 16.
    Verwer, S., Hammerschmidt, C.: flexfringe: a passive automaton learning package, pp. 638–642 (2017)Google Scholar
  17. 17.
    Oncina, J., García, P., Vidal, E.: Learning subsequential transducers for pattern recognition interpretation tasks. IEEE Trans. Pattern Anal. Mach. Intell. 15(5), 448–458 (1993)CrossRefGoogle Scholar
  18. 18.
    Giantamidis, G., Tripakis, S.: Learning Moore machines from input–output traces. In: Fitzgerald, J.S., Heitmeyer, C.L., Gnesi, S., Philippou, A. (eds.) 21st International Symposium on Formal Methods (FM 2016), Volume 9995 of LNCS, pp. 291–309 (2016)Google Scholar
  19. 19.
    Mens, I.-E., Maler, O.: Learning regular languages over large ordered alphabets. Log. Methods Comput. Sci. 11(3) (2015).  https://doi.org/10.2168/LMCS-11(3:13)2015
  20. 20.
    Argyros, G., Stais, I., Kiayias, A., Keromytis, A.D.: Back in black: towards formal, black box analysis of sanitizers and filters. In: IEEE Symposium on Security and Privacy, SP 2016, pp. 91–109 (2016)Google Scholar
  21. 21.
    Drews, S., D’Antoni, L.: Learning symbolic automata. In: Tools and Algorithms for the Construction and Analysis of Systems—23rd International Conference, TACAS 2017, volume 10205 of LNCS, pp. 173–189 (2017)Google Scholar
  22. 22.
    Lang, K.J., Pearlmutter, B.A., Price, R.A.: Results of the abbadingo one dfa learning competition and a new evidence-driven state merging algorithm. In: Honavar, V., Slutzki, G. (eds.) Grammatical Inference. Springer, Berlin (1998)Google Scholar
  23. 23.
    Walkinshaw, N., Lambeau, B., Damas, C., Bogdanov, K., Dupont, P.: Stamina: a competition to encourage the development and assessment of software model inference techniques. Empir. Softw. Eng. 18(4), 791–824 (2013)CrossRefGoogle Scholar
  24. 24.
    Verwer, S., Eyraud, R., Higuera, C.: Pautomac: a probabilistic automata and hidden markov models learning competition. Mach. Learn. 96(1), 129–154 (2014)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Jasper, M., Mues, M., Murtovi, A., Schlüter, M., Howar, F., Steffen, B., Schordan, M., Hendriks, D., Schiffelers, R., Kuppens, H., Vaandrager, F.W.: Rers 2019: combining synthesis with real-world models. In: Beyer, D., Huisman, M., Kordon, F., Steffen, B. (eds.) Tools and Algorithms for the Construction and Analysis of Systems, pp. 101–115. Springer International Publishing, Cham (2019)CrossRefGoogle Scholar
  26. 26.
    Moore, E.F.: Gedanken-experiments on sequential machines. In: Automata Studies, number 34. Princeton University Press (1956)Google Scholar
  27. 27.
    Gill, A.: State-identification experiments in finite automata. Inf. Control 4, 132–154 (1961)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Shahbaz, M., Groz, R.: Inferring mealy machines. In: FM 2009, pp. 207–222 (2009)Google Scholar
  30. 30.
    Jonsson, B.: Learning of automata models extended with data. In: SFM 2011, Advanced Lectures, pp. 327–349 (2011)Google Scholar
  31. 31.
    Cassel, S., Howar, F., Jonsson, B., Steffen, B.: Learning extended finite state machines. In: SEFM 2014, Proceedings, pp. 250–264 (2014)Google Scholar
  32. 32.
    Aarts, F., Vaandrager, F.: Learning I/O automata. In: CONCUR. Springer, pp. 71–85 (2010)Google Scholar
  33. 33.
    Howar, F., Steffen, B., Jonsson, B., Cassel, S.: Inferring canonical register automata. In: VMCAI 2012, Proceedings, pp. 251–266 (2012)Google Scholar
  34. 34.
    Aarts, F., Fiterau-Brostean, P., Kuppens, H., Vaandrager, F.W.: Learning register automata with fresh value generation. In: Theoretical Aspects of Computing—ICTAC, volume 9399 of LNCS, pp. 165–183 (2015)Google Scholar
  35. 35.
    Medhat, R., Ramesh, S., Bonakdarpour, B., Fischmeister, S.: A framework for mining hybrid automata from input/output traces. In: Embedded Software (EMSOFT), pp. 177–186 (2015)Google Scholar
  36. 36.
    Gold, E.M.: Complexity of automaton identification from given data. Inf. Control 37(3), 302–320 (1978)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Heule, M.J., Verwer, S.: Software model synthesis using satisfiability solvers. Empir. Softw. Eng. 18(4), 825–856 (2013)CrossRefGoogle Scholar
  38. 38.
    Ulyantsev, V., Zakirzyanov, I., Shalyto, A.: BFS-based symmetry breaking predicates for DFA identification. In: Language and Automata Theory and Applications (LATA), volume 8977 of LNCS. Springer, pp. 611–622 (2015)Google Scholar
  39. 39.
    Oncina, J., Garcia, P.: Identifying regular languages in polynomial time. In: Advances in Structural and Syntactic Pattern Recognition, pp. 99–108 (1992)Google Scholar
  40. 40.
    Dupont, P.: Incremental regular inference. In: ICGI-96, pp. 222–237 (1996)Google Scholar
  41. 41.
    Lang, K.J., Pearlmutter, B.A., Price, R.A.: Results of the abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. In: ICGI-98, pp. 1–12 (1998)Google Scholar
  42. 42.
    Beschastnikh, I., Brun, Y., Ernst, M.D., Krishnamurthy, A.: Inferring models of concurrent systems from logs of their behavior with csight. In: Proceedings of the 36th International Conference on Software Engineering, ICSE 2014. ACM, New York, NY, USA, pp. 468–479 (2014)Google Scholar
  43. 43.
    Verwer, S., de Weerdt, M., Witteveen, C.: A likelihood-ratio test for identifying probabilistic deterministic real-time automata from positive data. In: Sempere, J.M., García, P. (eds.) Grammatical Inference: Theoretical Results and Applications, pp. 203–216. Springer, Berlin (2010)CrossRefGoogle Scholar
  44. 44.
    Walkinshaw, N., Taylor, R., Derrick, J.: Inferring extended finite state machine models from software executions. Empir. Softw. Eng. 21(3), 811–853 (2016).  https://doi.org/10.1007/s10664-015-9367-7 CrossRefGoogle Scholar
  45. 45.
    Spichakova, M.: An approach to inference of finite state machines based on gravitationally-inspired search algorithm. Proc. Estonian Acad. Sci. 62(1), 39–46 (2013)CrossRefGoogle Scholar
  46. 46.
    Aleksandrov, A.V., Kazakov, S.V., Sergushichev, A.A., Tsarev, F.N., Shalyto, A.A.: The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior. J. Comput. Sys. Sc. Int. 52(3), 410–425 (2013)CrossRefGoogle Scholar
  47. 47.
    Buzhinsky, I.P., Ulyantsev, V.I., Chivilikhin, D.S., Shalyto, A.A.: Inducing finite state machines from training samples using ant colony optimization. J. Comput. Sys. Sc. Int. 53(2), 256–266 (2014)CrossRefGoogle Scholar
  48. 48.
    Meinke, K.: CGE: a sequential learning algorithm for mealy automata. In: Sempere, J.M., García, P. (eds.) Grammatical Inference: Theoretical Results and Applications, 10th International Colloquium, ICGI 2010, Valencia, Spain, September 13–16, 2010. Proceedings, volume 6339 of LNCS. Springer, pp. 148–162 (2010)Google Scholar
  49. 49.
    Veelenturf, L.P.J.: Inference of sequential machines from sample computations. IEEE Trans. Comput. 27(2), 167–170 (1978)MathSciNetCrossRefGoogle Scholar
  50. 50.
    Takahashi, K., Fujiyoshi, A., Kasai, T.: A polynomial time algorithm to infer sequential machines. Syst. Comput. Jpn. 34(1), 59–67 (2003)CrossRefGoogle Scholar
  51. 51.
    Biermann, A.W., Feldman, J.A.: On the synthesis of finite-state machines from samples of their behavior. IEEE Trans. Comput. 21(6), 592–597 (1972)MathSciNetCrossRefGoogle Scholar
  52. 52.
    Karthik, A.V., Ray, S., Nuzzo, P., Mishchenko, A., Brayton, R., Roychowdhury, J.: ABCD-NL: approximating continuous non-linear dynamical systems using purely Boolean models for analog/mixed-signal verification. In: ASP-DAC, pp. 250–255 (2014)Google Scholar
  53. 53.
    Grinchtein, O., Leucker, M.: Learning finite-state machines from inexperienced teachers. In: ICGI, pp. 344–345 (2006)Google Scholar
  54. 54.
    Leucker, M., Neider, D.: Learning minimal deterministic automata from inexperienced teachers. In: ISoLA, pp. 524–538 (2012)Google Scholar
  55. 55.
    Heitmeyer, C.L., Pickett, M., Leonard, E.I., Archer, M.M., Ray, I., Aha, D.W., Trafton, J.G.: Building high assurance human-centric decision systems. Autom. Softw. Eng. 22(2), 159–197 (2015)CrossRefGoogle Scholar
  56. 56.
    Ulyantsev, V., Buzhinsky, I., Shalyto, A.: Exact finite-state machine identification from scenarios and temporal properties. STTT 20(1), 35–55 (2018)CrossRefGoogle Scholar
  57. 57.
    Gulwani, S., Srivastava, S., Venkatesan, R.: Program analysis as constraint solving. In: PLDI’08. ACM, pp. 281–292 (2008)Google Scholar
  58. 58.
    Colón, M.A., Sankaranarayanan, S., Sipma, H.B.: Linear invariant generation using non-linear constraint solving. In: CAV. Springer, pp. 420–432 (2003)Google Scholar
  59. 59.
    Gupta, A., Rybalchenko, A.: Invgen: an efficient invariant generator. In: Computer Aided Verification, CAV. Springer, pp. 634–640 (2009)Google Scholar
  60. 60.
    Ackermann, C., Cleaveland, R., Huang, S., Ray, A., Shelton, C., Latronico, E.: Automatic requirement extraction from test cases. In: Runtime Verification, RV’10 (2010)Google Scholar
  61. 61.
    Jin, X., Donz, A., Deshmukh, J.V., Seshia, S.A.: Mining requirements from closed-loop control models. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34(11), 1704–1717 (2015)CrossRefGoogle Scholar
  62. 62.
    Lemieux, C., Park, D., Beschastnikh, I.: General LTL specification mining. In: Automated Software Engineering (ASE), pp. 81–92 (2015)Google Scholar
  63. 63.
    Ammons, G., Bodík, R., Larus, J.R.: Mining specifications. In: POPL’02. ACM, pp. 4–16 (2002)Google Scholar
  64. 64.
    Lee, D., Yannakakis, M.: Principles and methods of testing finite state machines—a survey. Proc. IEEE 84(8), 1090–1123 (1996)CrossRefGoogle Scholar
  65. 65.
    Chow, T.S.: Testing software design modeled by finite-state machines. IEEE Trans. Softw. Eng. 4(3), 178–187 (1978)CrossRefGoogle Scholar
  66. 66.
    Dorofeeva, R., El-Fakih, K., Maag, S., Cavalli, A.R., Yevtushenko, N.: Fsm-based conformance testing methods: a survey annotated with experimental evaluation. Inf. Softw. Technol. 52(12), 1286–1297 (2010)CrossRefGoogle Scholar
  67. 67.
    Berg, T., Grinchtein, O., Jonsson, B., Leucker, M., Raffelt, H., Steffen, B.: On the correspondence between conformance testing and regular inference. In: FASE, volume 3442 of LNCS. Springer, pp. 175–189 (2005)Google Scholar
  68. 68.
    Sorower, M.S.: A literature survey on algorithms for multi-label learning. Technical report (2010)Google Scholar
  69. 69.
    Coste, F., Nicolas, J.: ICGI-98, chapter How considering incompatible state mergings may reduce the DFA induction search tree. Springer, pp. 199–210 (1998)Google Scholar
  70. 70.
    The D Programming Language. https://dlang.org/
  71. 71.
    Walkinshaw, N., Bogdanov, K.: Inferring finite-state models with temporal constraints. In: ASE, pp. 248–257 (2008)Google Scholar
  72. 72.
    Tsarev, F., Egorov, K.: Finite state machine induction using genetic algorithm based on testing and model checking. In: 13th Annual Genetic and Evolutionary Computation Conference, GECCO, pp. 759–762 (2011)Google Scholar
  73. 73.
    Lo, D., Khoo, S.-C.: Smartic: towards building an accurate, robust and scalable specification miner. In: FSE. ACM, New York, NY, USA, pp. 265–275 (2006)Google Scholar
  74. 74.
    Akram, H.I., de la Higuera, C., Xiao, H., Eckert, C.: Grammatical inference algorithms in matlab. In: ICGI’10. Springer, pp. 262–266 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Georgios Giantamidis
    • 1
    • 2
    Email author
  • Stavros Tripakis
    • 3
  • Stylianos Basagiannis
    • 1
  1. 1.United Technologies Research Centre IrelandCorkIreland
  2. 2.Aalto UniversityOtaniemiFinland
  3. 3.Northeastern UniversityBostonUSA

Personalised recommendations