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Model-Based Testing Without Models: The TodoMVC Case Study

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ModelEd, TestEd, TrustEd

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10500))

Abstract

Web applications define the interface to many of the businesses and services that we interact with and use on a daily basis. The technology stack enabling these applications is constantly changing and applications are accessed from a plethora of different devices. Automated testing of the behavior of applications is a promising strategy for reducing the manual effort that has to be spent on ensuring a consistent user experience across devices. Unfortunately, specifications or models of the desired behavior often do not exist. Model-based testing without models (aka learning-based testing) tries to overcome this hurdle by integrating model learning and model-based testing. In this paper, we sketch the ALEX tool [1, 11] for learning-based testing of web application and demonstrate its operation on benchmarks from the TodoMVC project. Our learning-based conformance analysis reveals that 7 of 27 Todo-apps exhibit behavior that differs from the majority of implementations.

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Notes

  1. 1.

    http://learnlib.de/.

  2. 2.

    http://learnlib.github.io/alex/.

  3. 3.

    http://www.seleniumhq.org/.

  4. 4.

    http://todomvc.com/.

  5. 5.

    This has been enforced by adding code to the implementations of test inputs that checks whether a task is absent or created, but currently not visible — similar to a test purpose.

  6. 6.

    https://sites.google.com/a/chromium.org/chromedriver/.

  7. 7.

    http://learnlib.github.io/alex/book/1.2.1/contents/examples/todomvc/index.html.

References

  1. Automata learning experience (2016). http://learnlib.github.io/alex/. Accessed 23 Oct 2016

  2. Todomvc (2016). http://todomvc.com/. Accessed 23 Oct 2016

  3. Todomvc specification (2016). https://github.com/tastejs/todomvc/blob/master/app-spec.md. Accessed 21 Oct 2016

  4. Aarts, F., Jonsson, B., Uijen, J.: Generating models of infinite-state communication protocols using regular inference with abstraction. In: Petrenko, A., Simão, A., Maldonado, J.C. (eds.) ICTSS 2010. LNCS, vol. 6435, pp. 188–204. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16573-3_14

    Chapter  Google Scholar 

  5. Aarts, F., Kuppens, H., Tretmans, J., Vaandrager, F., Verwer, S.: Improving active mealy machine learning for protocol conformance testing. Mach. Learn. 96(1), 189–224 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  6. Aarts, F., Kuppens, H., Tretmans, J., Vaandrager, F.W., Verwer, S.: Learning and testing the bounded retransmission protocol. In: Proceedings of the Eleventh International Conference on Grammatical Inference, ICGI 2012. University of Maryland, College Park, USA, 5–8 September 2012, pp. 4–18 (2012)

    Google Scholar 

  7. Alur, R., Madhusudan, P.: Visibly pushdown languages. In: Proceedings of the Thirty-sixth Annual ACM Symposium on Theory of Computing, STOC 2004, pp. 202–211. ACM, New York, NY, USA (2004)

    Google Scholar 

  8. Angluin, D.: Learning regular sets from queries and counter examples. Inf. Comput. 75(2), 87–106 (1987)

    Article  MATH  Google Scholar 

  9. Argyros, G., Stais, I., Kiayias, A., Keromytis, A.D.: Back in black: towards formal, black box analysis of sanitizers and filters. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 91–109, May 2016

    Google Scholar 

  10. Avasarala, S.: Selenium WebDriver Practical Guide. Packt Publishing, Birmingham (2014). ISBN 9781782168850

    Google Scholar 

  11. Bainczyk, A., Schieweck, A., Isberner, M., Margaria, T., Neubauer, J., Steffen, B.: ALEX: mixed-mode learning of web applications at ease. In: Margaria, T., Steffen, B. (eds.) ISoLA 2016. LNCS, vol. 9953, pp. 655–671. Springer, Cham (2016). doi:10.1007/978-3-319-47169-3_51

    Chapter  Google Scholar 

  12. Belinfante, A.: JTorX: a tool for on-line model-driven test derivation and execution. In: Esparza, J., Majumdar, R. (eds.) TACAS 2010. LNCS, vol. 6015, pp. 266–270. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12002-2_21

    Chapter  Google Scholar 

  13. Berg, T., Grinchtein, O., Jonsson, B., Leucker, M., Raffelt, H., Steffen, B.: On the correspondence between conformance testing and regular inference. In: Cerioli, M. (ed.) FASE 2005. LNCS, vol. 3442, pp. 175–189. Springer, Heidelberg (2005). doi:10.1007/978-3-540-31984-9_14

    Chapter  Google Scholar 

  14. Bertolino, A., Calabrò, A., Merten, M., Steffen, B.: Never-stop learning: continuous validation of learned models for evolving systems through monitoring. ERCIM News 2012(88), 28–29 (2012)

    Google Scholar 

  15. Brinksma, E., Tretmans, J.: Testing transition systems: an annotated bibliography. In: Cassez, F., Jard, C., Rozoy, B., Ryan, M.D. (eds.) MOVEP 2000. LNCS, vol. 2067, pp. 187–195. Springer, Heidelberg (2001). doi:10.1007/3-540-45510-8_9

    Chapter  Google Scholar 

  16. Cassel, S., Howar, F., Jonsson, B., Steffen, B.: Active learning for extended finite state machines. Formal Aspects Comput. 28(2), 233–263 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  17. Choi, W., Necula, G., Sen, K.: Guided GUI testing of android apps with minimal restart and approximate learning. SIGPLAN Not. 48(10), 623–640 (2013)

    Article  Google Scholar 

  18. Chow, T.S.: Testing software design modeled by finite-state machines. IEEE Trans. Softw. Eng. SE–4(3), 178–187 (1978)

    Article  MATH  Google Scholar 

  19. Dinca, I., Ipate, F., Mierla, L., Stefanescu, A.: Learn and test for event-B – A rodin plugin. In: Derrick, J., Fitzgerald, J., Gnesi, S., Khurshid, S., Leuschel, M., Reeves, S., Riccobene, E. (eds.) ABZ 2012. LNCS, vol. 7316, pp. 361–364. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30885-7_32

    Chapter  Google Scholar 

  20. Drews, S., D’Antoni, L.: Learning symbolic automata. In: Legay, A., Margaria, T. (eds.) TACAS 2017. LNCS, vol. 10205, pp. 173–189. Springer, Heidelberg (2017). doi:10.1007/978-3-662-54577-5_10

    Chapter  Google Scholar 

  21. Flanagan, D.: JavaScript: The Definitive Guide: Activate Your Web Pages (Definitive Guides), 6th edn. O’Reilly Media, Sebastopol (2011)

    Google Scholar 

  22. Fujiwara, S., von Bochmann, G., Khendek, F., Amalou, M., Ghedamsi, A.: Test selection based on finite state models. IEEE Trans. Softw. Eng. 17(6), 591–603 (1991)

    Article  Google Scholar 

  23. Hagerer, A., Hungar, H., Niese, O., Steffen, B.: Model generation by moderated regular extrapolation. In: Kutsche, R.-D., Weber, H. (eds.) FASE 2002. LNCS, vol. 2306, pp. 80–95. Springer, Heidelberg (2002). doi:10.1007/3-540-45923-5_6

    Chapter  Google Scholar 

  24. Hagerer, A., Margaria, T., Niese, O., Steffen, B., Brune, G., Ide, H.-D.: Efficient regression testing of CTI-systems: testing a complex call-center solution. Ann. Rev. Commun. Int. Eng. Consortium (IEC) 55, 1033–1040 (2001)

    Google Scholar 

  25. 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). doi:10.1007/978-3-642-18275-4_19

    Chapter  Google Scholar 

  26. Isberner, M.: Foundations of active automata learning: an algorithmic perspective. Ph.D. thesis. TU Dortmund University, October 2015

    Google Scholar 

  27. Isberner, M., Howar, F., Steffen, B.: Inferring automata with state-local alphabet abstractions. In: Brat, G., Rungta, N., Venet, A. (eds.) NFM 2013. LNCS, vol. 7871, pp. 124–138. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38088-4_9

    Chapter  Google Scholar 

  28. Isberner, M., Howar, F., Steffen, B.: Learning register automata: from languages to program structures. Mach. Learn. 96, 1–34 (2013)

    MathSciNet  MATH  Google Scholar 

  29. Isberner, M., Howar, F., Steffen, B.: The TTT algorithm: a redundancy-free approach to active automata learning. In: Bonakdarpour, B., Smolka, S.A. (eds.) RV 2014. LNCS, vol. 8734, pp. 307–322. Springer, Cham (2014). doi:10.1007/978-3-319-11164-3_26

    Google Scholar 

  30. Isberner, M., Howar, F., Steffen, B.: The open-source learnlib. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 487–495. Springer, Cham (2015). doi:10.1007/978-3-319-21690-4_32

    Chapter  Google Scholar 

  31. Maler, O., Mens, I.-E.: Learning regular languages over large alphabets. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014. LNCS, vol. 8413, pp. 485–499. Springer, Heidelberg (2014). doi:10.1007/978-3-642-54862-8_41

    Chapter  Google Scholar 

  32. Margaria, T., Steffen, B.: Simplicity as a driver for agile innovation. Computer 43(6), 90–92 (2010)

    Article  Google Scholar 

  33. Mealy, G.H.: A method for synthesizing sequential circuits. Bell Syst. Tech. J. 34(5), 1045–1079 (1955)

    Article  MathSciNet  Google Scholar 

  34. Meinke, K., Sindhu, M.A.: Lbtest: a learning-based testing tool for reactive systems. In: Sixth IEEE International Conference on Software Testing, Verification and Validation, ICST 2013, Luxembourg, 18–22 March 2013, pp. 447–454 (2013)

    Google Scholar 

  35. Merten, M., Steffen, B., Howar, F., Margaria, T.: Next generation learnlib. In: Abdulla, P.A., Leino, K.R.M. (eds.) TACAS 2011. LNCS, vol. 6605, pp. 220–223. Springer, Heidelberg (2011). doi:10.1007/978-3-642-19835-9_18

    Chapter  Google Scholar 

  36. Raffelt, H., Merten, M., Steffen, B., Margaria, T.: Dynamic testing via automata learning. Int. J. Softw. Tools Technol. Trans. (STTT) 11(4), 307–324 (2009)

    Article  Google Scholar 

  37. Raffelt, H., Steffen, B., Berg, T., Margaria, T.: LearnLib: a framework for extrapolating behavioral models. Int. J. Softw. Tools Technol. Trans. (STTT) 11(5), 393–407 (2009)

    Article  Google Scholar 

  38. Shahbaz, M., Groz, R.: Analysis and testing of black-box component-based systems by inferring partial models. Softw. Test. Verif. Reliab. 24(4), 253–288 (2014)

    Article  Google Scholar 

  39. Steffen, B., Howar, F., Merten, M.: Introduction to active automata learning from a practical perspective. In: Bernardo, M., Issarny, V. (eds.) SFM 2011. LNCS, vol. 6659, pp. 256–296. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21455-4_8

    Chapter  Google Scholar 

  40. Timmer, M., Brinksma, E., Stoelinga, M.: Model-based testing. In: Software and Systems Safety - Specification and Verification, pp. 1–32. IOS Press (2011)

    Google Scholar 

  41. Tretmans, J.: Model-based testing and some steps towards test-based modelling. In: Bernardo, M., Issarny, V. (eds.) SFM 2011. LNCS, vol. 6659, pp. 297–326. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21455-4_9

    Chapter  Google Scholar 

  42. Tretmans, J., Brinksma, E.: Torx: automated model-based testing. In: Hartman, A., Dussa-Ziegler, K. (ed.) First European Conference on Model-Driven Software Engineering, pp. 31–43, December 2003

    Google Scholar 

  43. Vaandrager, F.: Model learning. Commun. ACM 60(2), 86–95 (2017)

    Article  Google Scholar 

  44. Windmüller, S., Neubauer, J., Steffen, B., Howar, F., Bauer, O.: Active continuous quality control. In: Proceedings of the 16th International ACM Sigsoft Symposium on Component-based Software Engineering, CBSE 2013, pp. 111–120. ACM New York, NY, USA (2013)

    Google Scholar 

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Authors and Affiliations

  1. Dortmund University of Technology, Dortmund, Germany

    Alexander Bainczyk, Alexander Schieweck, Bernhard Steffen & Falk Howar

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  1. Alexander Bainczyk
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  2. Alexander Schieweck
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  3. Bernhard Steffen
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  4. Falk Howar
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Correspondence to Alexander Bainczyk or Alexander Schieweck .

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Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Joost-Pieter Katoen

  2. University of Twente, Enschede, The Netherlands

    Rom Langerak

  3. University of Twente, Enschede, The Netherlands

    Arend Rensink

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Bainczyk, A., Schieweck, A., Steffen, B., Howar, F. (2017). Model-Based Testing Without Models: The TodoMVC Case Study. In: Katoen, JP., Langerak, R., Rensink, A. (eds) ModelEd, TestEd, TrustEd. Lecture Notes in Computer Science(), vol 10500. Springer, Cham. https://doi.org/10.1007/978-3-319-68270-9_7

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