A distributed bug analyzer based on user-interaction features for mobile apps
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http://hdl.handle.net/10045/102440
Título: | A distributed bug analyzer based on user-interaction features for mobile apps |
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Autor/es: | Méndez-Porras, Abel | Méndez-Marín, Giovanni | Tablada-Rojas, Alberto | Nieto-Hidalgo, Mario | García-Chamizo, Juan Manuel | Jenkins, Marcelo | Martínez, Alexandra |
Grupo/s de investigación o GITE: | Informática Industrial y Redes de Computadores |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Distributed bug analyzer | User-interaction features | Digital imaging processing | Interest points | Automated testing |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 2-feb-2017 |
Editor: | Springer Berlin Heidelberg |
Cita bibliográfica: | Journal of Ambient Intelligence and Humanized Computing. 2017, 8: 579-591. doi:10.1007/s12652-016-0435-7 |
Resumen: | Developers must spend more effort and attention on the processes of software development to deliver quality applications to the users. Software testing and automation play a strategic role in ensuring the quality of mobile applications. This paper proposes and evaluates a Distributed Bug Analyzer based on user-interaction features that uses digital imaging processing to find bugs. Our Distributed Bug Analyzer detects bugs by comparing the similarity between images taken before and after an user-interaction feature occurs. An interest point detector and descriptor is used for image comparison. To evaluate the Distribute Bug Analyzer, we conducted a case study with 38 randomly selected mobile applications. First, we identified user-interaction bugs by manually testing the applications. Images were captured before and after applying each user-interaction feature. Then, image pairs were processed (using SURF) to obtain interest points, from which a similarity percentage was computed, to identify the presence of bugs. We used a Master Computer, a Storage Test Database, and four Slave Computers to evaluate the Distributed Bug Analyzer. We performed 360 tests of user-interaction features in total. We found 79 bugs when manually testing user-interaction features, and 69 bugs when using digital imaging processing to detect bugs with a threshold fixed at 92.5% of similarity. Distributed Bug Analyzer evenly distributed tests that are pending in the Storage Test Database between the Slave Computers. Slave Computers 1, 2, 3, and 4 processed 21, 20, 23, and 36% of image pair respectively. |
Patrocinador/es: | This research was supported by the Costa Rican Ministry of Science, Technology and Telecommunications (MICITT). |
URI: | http://hdl.handle.net/10045/102440 |
ISSN: | 1868-5137 (Print) | 1868-5145 (Online) |
DOI: | 10.1007/s12652-016-0435-7 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © Springer-Verlag Berlin Heidelberg 2017 |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s12652-016-0435-7 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2017_Mendez-Porras_etal_JAmbientIntellHumanComput_final.pdf | Versión final (acceso restringido) | 2,94 MB | Adobe PDF | Abrir Solicitar una copia |
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