Using Model-Based Testing to Reduce Test Automation Technical Debt: An Industrial Experience Report

  • Thomas HuertasEmail author
  • Christian Quesada-LópezEmail author
  • Alexandra MartínezEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)


Technical debt is the metaphor used to describe the effect of incomplete or immature software artifacts that bring short-term benefits to projects, but may have to be paid later with interest. Software testing cost is proven to be high due to the time (and resource)-consuming activities involved. Test automation is a strategy that can potentially reduce this cost and provide savings to the software development process. The lack or poor implementation of a test automation approach derives in test automation debt. The goal of this paper is to report our experience using a model-based testing (MBT) approach on two industrial legacy applications and assess its impact on test automation debt reduction. We selected two legacy systems exhibiting high test automation debt, then used a MBT tool to model the systems and automatically generate test cases. We finally assessed the impact of this approach on the test automation technical debt by analyzing the code coverage attained by the tests and by surveying development team perceptions. Our results show that test automation debt was reduced by adding a suite of automated tests and reaching more than 75% of code coverage. Moreover, the development team agrees in that MBT could help reduce other types of technical debt present in legacy systems, such as documentation debt and design debt. Although our results are promising, more studies are needed to validate our findings.


Test automation debt Model-based testing Legacy systems 


  1. 1.
    Alves, N.S.R., et al.: Identification and management of technical debt: a systematic mapping study. Inf. Softw. Technol. 70(Supplement C), 100–121 (2016). ISSN 0950-5849CrossRefGoogle Scholar
  2. 2.
    Christmann, S., et al.: ISTQB® Foundation Level Certified Model-Based Tester Syllabus. International Software Testing Qualifications Board Foundation Level Working Group (2015)Google Scholar
  3. 3.
    Cunningham, W.: The WyCash portfolio management system. In: Addendum to the Proceedings on Object-Oriented Programming Systems, Languages, and Applications (Addendum), OOPSLA 1992, pp. 29–30. ACM, Vancouver (1992)Google Scholar
  4. 4.
    Holvitie, J., Leppänen, V., Hyrynsalmi, S.: Technical debt and the effect of agile software development practices on it - an industry practitioner survey. In: 2014 Sixth International Workshop on Managing Technical Debt, pp. 35–42 (2014)Google Scholar
  5. 5.
    de Jesus, J.S., de Melo, A.C.V.: Technical debt and the software project characteristics. A repository-based exploratory analysis. In: 2017 IEEE 19th Conference on Business Informatics (CBI), vol. 01, pp. 444–453 (2017)Google Scholar
  6. 6.
    Masri, W., Zaraket, F.A.: Coverage-based software testing: beyond basic test requirements. Adv. Comput. 103, 79–142 (2016)CrossRefGoogle Scholar
  7. 7.
    Trumler, W., Paulisch, F.: How “Specification by Example” and test-driven development help to avoid technial debt. In: 2016 IEEE 8th International Workshop on Managing Technical Debt (MTD), pp. 1– 8 (2016)Google Scholar
  8. 8.
    Utting, M., Pretschner, A., Legeard, B.: A taxonomy of model-based testing approaches. Softw. Test. Verif. Reliab. 22(5), 297–312 (2012)CrossRefGoogle Scholar
  9. 9.
    Wendland, M.-F., et al.: Model-based testing in legacy software modernization: an experience report. In: Proceedings of the 2013 International Workshop on Joining AcadeMiA and Industry Contributions to Testing Automation, JAMAICA 2013, pp. 35–40. ACM, Lugano (2013)Google Scholar
  10. 10.
    Wiklund, K., et al.: Technical Debt in Test Automation, April 2012Google Scholar
  11. 11.
    Xu, D.: A tool for automated test code generation from high-level petri nets. In: Kristensen, L.M., Petrucci, L. (eds.) Applications and Theory of Petri Nets. PETRI NETS 2011, pp. 308–317. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad de Costa RicaSan JoséCosta Rica

Personalised recommendations