TesCaV: An Approach for Learning Model-Based Testing and Coverage in Practice

Beatriz Marín; Sofía Alarcón; Giovanni Giachetti; Monique Snoeck

doi:10.1007/978-3-030-50316-1_18

TesCaV: An Approach for Learning Model-Based Testing and Coverage in Practice

Beatriz Marín, Sofía Alarcón, Giovanni Giachetti, Monique Snoeck

Producción científica: Contribución a los tipos de informe/libro › Contribución a la conferencia › revisión exhaustiva

10 Citas (Scopus)

Resumen

Academy and industry permanently remark the importance of software-testing techniques to improve software quality and to reduce development and maintenance costs. A testing method to be considered for this purpose is Model-Based Testing (MBT), which generates test cases from a model that represents the structure and the behavior of the system to be developed. The generated test suite is easier to maintain and adapt to changes in requirements or evolution of the developed system. However, teaching and learning MBT techniques are not easy tasks; students need to know the different testing techniques to assure that the requirements are fulfilled as well as to identify any failure in the software system modeled. In this work, we present TesCaV, an MBT teaching tool for university students, which is based on a model-driven technology for the automatic software generation from UML diagrams. TesCaV allows validating the test cases defined by students and graphically determines the level of testing coverage over the system modeled. Preliminary results show TesCaV as a promising approach for MBT teaching/learning processes.

Idioma original	Inglés
Título de la publicación alojada	Research Challenges in Information Science - 14th International Conference, RCIS 2020, Proceedings
Editores	Fabiano Dalpiaz, Jelena Zdravkovic, Pericles Loucopoulos
Editorial	Springer
Páginas	302-317
Número de páginas	16
ISBN (versión impresa)	9783030503154
DOI	https://doi.org/10.1007/978-3-030-50316-1_18
Estado	Publicada - 2020
Publicado de forma externa	Sí
Evento	14th International Conference on Research Challenges in Information Sciences, RCIS 2020 - Limassol, Chipre Duración: 23 sep. 2020 → 25 sep. 2020

Serie de la publicación

Nombre	Lecture Notes in Business Information Processing
Volumen	385 LNBIP
ISSN (versión impresa)	1865-1348
ISSN (versión digital)	1865-1356

Conferencia

Conferencia	14th International Conference on Research Challenges in Information Sciences, RCIS 2020
País/Territorio	Chipre
Ciudad	Limassol
Período	23/09/20 → 25/09/20

Áreas temáticas de ASJC Scopus

Sistemas de gestión de la información
Ingeniería de control y sistemas
Gestión internacional y de empresa
Sistemas de información
Modelización y simulación
Gestión y sistemas de información

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10.1007/978-3-030-50316-1_18

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Marín, B., Alarcón, S., Giachetti, G., & Snoeck, M. (2020). TesCaV: An Approach for Learning Model-Based Testing and Coverage in Practice. En F. Dalpiaz, J. Zdravkovic, & P. Loucopoulos (Eds.), Research Challenges in Information Science - 14th International Conference, RCIS 2020, Proceedings (pp. 302-317). (Lecture Notes in Business Information Processing; Vol. 385 LNBIP). Springer. https://doi.org/10.1007/978-3-030-50316-1_18

Marín, Beatriz ; Alarcón, Sofía ; Giachetti, Giovanni et al. / TesCaV : An Approach for Learning Model-Based Testing and Coverage in Practice. Research Challenges in Information Science - 14th International Conference, RCIS 2020, Proceedings. editor / Fabiano Dalpiaz ; Jelena Zdravkovic ; Pericles Loucopoulos. Springer, 2020. pp. 302-317 (Lecture Notes in Business Information Processing).

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abstract = "Academy and industry permanently remark the importance of software-testing techniques to improve software quality and to reduce development and maintenance costs. A testing method to be considered for this purpose is Model-Based Testing (MBT), which generates test cases from a model that represents the structure and the behavior of the system to be developed. The generated test suite is easier to maintain and adapt to changes in requirements or evolution of the developed system. However, teaching and learning MBT techniques are not easy tasks; students need to know the different testing techniques to assure that the requirements are fulfilled as well as to identify any failure in the software system modeled. In this work, we present TesCaV, an MBT teaching tool for university students, which is based on a model-driven technology for the automatic software generation from UML diagrams. TesCaV allows validating the test cases defined by students and graphically determines the level of testing coverage over the system modeled. Preliminary results show TesCaV as a promising approach for MBT teaching/learning processes.",

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Marín, B, Alarcón, S, Giachetti, G & Snoeck, M 2020, TesCaV: An Approach for Learning Model-Based Testing and Coverage in Practice. En F Dalpiaz, J Zdravkovic & P Loucopoulos (eds.), Research Challenges in Information Science - 14th International Conference, RCIS 2020, Proceedings. Lecture Notes in Business Information Processing, vol. 385 LNBIP, Springer, pp. 302-317, 14th International Conference on Research Challenges in Information Sciences, RCIS 2020, Limassol, Chipre, 23/09/20. https://doi.org/10.1007/978-3-030-50316-1_18

TesCaV: An Approach for Learning Model-Based Testing and Coverage in Practice. / Marín, Beatriz; Alarcón, Sofía; Giachetti, Giovanni et al.
Research Challenges in Information Science - 14th International Conference, RCIS 2020, Proceedings. ed. / Fabiano Dalpiaz; Jelena Zdravkovic; Pericles Loucopoulos. Springer, 2020. p. 302-317 (Lecture Notes in Business Information Processing; Vol. 385 LNBIP).

Producción científica: Contribución a los tipos de informe/libro › Contribución a la conferencia › revisión exhaustiva

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T1 - TesCaV

T2 - 14th International Conference on Research Challenges in Information Sciences, RCIS 2020

AU - Marín, Beatriz

AU - Alarcón, Sofía

AU - Giachetti, Giovanni

AU - Snoeck, Monique

PY - 2020

Y1 - 2020

N2 - Academy and industry permanently remark the importance of software-testing techniques to improve software quality and to reduce development and maintenance costs. A testing method to be considered for this purpose is Model-Based Testing (MBT), which generates test cases from a model that represents the structure and the behavior of the system to be developed. The generated test suite is easier to maintain and adapt to changes in requirements or evolution of the developed system. However, teaching and learning MBT techniques are not easy tasks; students need to know the different testing techniques to assure that the requirements are fulfilled as well as to identify any failure in the software system modeled. In this work, we present TesCaV, an MBT teaching tool for university students, which is based on a model-driven technology for the automatic software generation from UML diagrams. TesCaV allows validating the test cases defined by students and graphically determines the level of testing coverage over the system modeled. Preliminary results show TesCaV as a promising approach for MBT teaching/learning processes.

AB - Academy and industry permanently remark the importance of software-testing techniques to improve software quality and to reduce development and maintenance costs. A testing method to be considered for this purpose is Model-Based Testing (MBT), which generates test cases from a model that represents the structure and the behavior of the system to be developed. The generated test suite is easier to maintain and adapt to changes in requirements or evolution of the developed system. However, teaching and learning MBT techniques are not easy tasks; students need to know the different testing techniques to assure that the requirements are fulfilled as well as to identify any failure in the software system modeled. In this work, we present TesCaV, an MBT teaching tool for university students, which is based on a model-driven technology for the automatic software generation from UML diagrams. TesCaV allows validating the test cases defined by students and graphically determines the level of testing coverage over the system modeled. Preliminary results show TesCaV as a promising approach for MBT teaching/learning processes.

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KW - Lessons learned

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PB - Springer

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ER -

Marín B, Alarcón S, Giachetti G, Snoeck M. TesCaV: An Approach for Learning Model-Based Testing and Coverage in Practice. En Dalpiaz F, Zdravkovic J, Loucopoulos P, editores, Research Challenges in Information Science - 14th International Conference, RCIS 2020, Proceedings. Springer. 2020. p. 302-317. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-030-50316-1_18

TesCaV: An Approach for Learning Model-Based Testing and Coverage in Practice

Resumen

Serie de la publicación

Conferencia

Áreas temáticas de ASJC Scopus

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Huella

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