Abstract
Model-based testing (MBT) is an approach that automates the design and generation of test cases based on a model that represents the system under test. MBT can reduce the cost of software testing and improve the quality of systems in the industry. The goal of this study is to evaluate the use of MBT in an industrial project with the purpose of analyzing its efficiency, efficacy and acceptance by software engineers. A case study was conducted where six software engineers modeled one module of a system, and then generated and executed the test cases using an MBT tool. Our results show that participants were able to model at least four functional requirements each, in a period of 20 to 60 min, reaching a code coverage between 39% and 59% of the system module. We discussed relevant findings about the completeness of the models and common mistakes made during the modeling and concretization phases. Regarding the acceptance of MBT by participants, our results suggest that while they saw value in the MBT approach, they were not satisfied with the tool used (MISTA), because it did not support key industry needs.
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References
Utting, M., Pretschner, A., Legeard, B.: A taxonomy of model-based testing approaches. Softw. Test. Verification Reliab. 22, 297–312 (2012)
Biswas, S., Kaiser, M.S., Mamun, S.: Applying ant colony optimization in software testing to generate prioritized optimal path and test data. In: 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–6 (2015)
Utting, M., Legeard, B.: Practical Model-Based Testing: A Tools Approach (2006)
Villalobos-Arias, L., Quesada-López, C., Martinez, A.: A tertiary study on model-based testing areas, tools and challenges: preliminary results (2018)
Weißleder, S., Schlingloff, H.: An evaluation of model-based testing in embedded applications. In: IEEE International Conference on Software Testing, Verification, and Validation (2014)
Ergun, B., Gebizli, C., Sozer, H.: Format: a tool for adapting test models based on feature models. In: IEEE 41st Annual Computer Software and Applications Conference (2017)
Herpel, H.J., et al.: Model based testing of satellite on-board software — an industrial use case. In: Aerospace Conference (2016)
Gebizli, C.S., Sözer, H.: Automated refinement of models for model-based testing using exploratory testing. Softw. Qual. J. 25(3), 979–1005 (2017)
Schulze, C., et al.: Model generation to support model-based testing applied on the NASA DAT web-application - an experience report (2015)
Bettiga, D., Lamberti, L.: Exploring the adoption process of personal technologies: a cognitive-affective approach. J. High Technol. Manag. Res. 28(2), 179–187 (2017)
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)
Wohlin, C., et al.: Experimentation in Software Engineering (2012)
Runeson, P., et al.: Case Study Research in Software Engineering: Guidelines and Examples (2012)
Xu, D.: A tool for automated test code generation from high-level petri nets (2011)
SeleniumHQ: SeleniumHQ. https://www.seleniumhq.org. Accessed 07 Mar 2018
Masri, W., Zaraket, F.A.: Coverage-based software testing: beyond basic test requirements. In: Advances in Computers, chap. 4, pp. 79–142 (2016)
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Vásquez, R.O., Quesada-López, C., Martínez, A. (2019). Evaluating Model-Based Testing in an Industrial Project: An Experience Report. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_29
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DOI: https://doi.org/10.1007/978-3-030-11890-7_29
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