Implementation patterns to extend the search-based reduction Model of MTest.search

• Autores: Alejandro Miguel Güemes-Esperón, Martha Dunia Delgado Dapena , Mailyn Moreno Espino, José Miguel Loor Intriago
• Localización: Revista Cubana de Ciencias Informáticas, ISSN-e 2227-1899, Vol. 16, Nº. 2, 2022
• Idioma: inglés
• Títulos paralelos:
  • Patrones de implementación para extender el Modelo de reducción basado en búsquedas de MTest.search
• Enlaces
  • Texto completo
• Resumen
  • español

    El proceso de generación de casos de pruebas contribuye a la calidad de los productos de software, detectando la mayor cantidad de errores posibles. La etapa de diseño de los casos de pruebas a partir de valores de prueba, constituye una tarea desafiante, por lo que es necesaria su automatización para elevar la efectividad para detectar errores y disminuir el costo. Algoritmos metaheurísticos como algoritmos genéticos, recocido simulado y búsqueda tabú se han aplicado con éxito para resolver la explosión combinatoria de la generación automática de la suite de prueba. El modelo MTest.search para la generación automática de pruebas unitarias tiene definidos mecanismos de extensión del modelo de dominio, de prueba y de ejecución. En este trabajo se presentan patrones de implementación para extender el modelo de reducción basado en búsquedas. Esta propuesta permite incorporar nuevas configuraciones del problema de optimización para generar combinaciones de valores de pruebas obteniendo suites de pruebas reducidas. Para validar la solución propuesta se definieron casos de estudios a partir del análisis de extensiones implementadas siguiendo los patrones definidos en este trabajo y de los casos de pruebas resultantes. En las extensiones implementadas se introdujo el criterio de significación de los valores de prueba y de los caminos/escenarios para obtener suites de pruebas reducidas con mayor potencialidad para detectar errores.

  • English

    The process of generating test cases contributes to the quality of software products, detecting as many errors as possible. The design stage of test cases from test values is a challenging task, so its automation is necessary to increase the effectiveness to detect errors and reduce cost. Metaheuristic algorithms such as genetic algorithms, simulated annealing, and tabu search have been successfully applied to solve the combinatorial explosion of automatic test suite generation. The MTest.search model for automatic unit test generation has domain model extension, test, and execution mechanisms defined. In this work, implementation patterns are presented to extend the search-based reduction model. This proposal allows incorporating new configurations of the optimization problem to generate combinations of test values obtaining reduced test suites. To validate the proposed solution, case studies were defined based on the analysis of extensions implemented following the patterns defined in this work and the resulting test cases. In the implemented extensions, the significance criterion of the test values and of the paths/scenarios was introduced to obtain reduced test suites with greater potential to detect errors.

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