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dc.contributor.authorMéndez-Porras, Abel
dc.contributor.authorNieto-Hidalgo, Mario
dc.contributor.authorGarcía-Chamizo, Juan-Manuel
dc.contributor.authorJenkins, Marcelo
dc.contributor.authorMartínez-Porras, Alexander
dc.date.accessioned2017-06-05T20:00:31Z
dc.date.available2017-06-05T20:00:31Z
dc.date.issued2015
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-319-26401-1_4es
dc.identifier.isbn978-331926400-4
dc.identifier.issn03029743
dc.identifier.urihttps://hdl.handle.net/2238/7190
dc.descriptionhttps://www.scopus.com/inward/record.url?eid=2-s2.0-84952312195&partnerID=40&md5=c66b3e19a171bc194ce49772b590487bes
dc.description.abstractMobile applications have become popular work tools. Portability and ease of Internet connectivity are characteristics that favor this adoption. However, mobile applications sometimes incorrectly process events associated with the user-interaction features. These features include content presentation or navigation. Rotating the devices, and gestures such as scroll or zoom into screens are some examples. There is a need to assess the quality with which mobile applications are processing these user-interaction features in order to improve their performance. In this paper, we present a top-down design approach for an automated testing framework for mobile applications. Our framework integrates digital image processing, GUI information, and historical bug information to identify new bugs based on user-interaction features. Our framework captures images before and after applying the user-interaction features and uses the SURF algorithm to identify interest points in each image. We compared interest points to note differences on the screens before and after applying the user-interaction features. This differences helps to find bugs in mobile applications. The first results show that it is feasible to identify bugs with user-interaction features using the proposed technique.es
dc.language.isoenges
dc.publisherSpringer Verlages
dc.rightsacceso abierto*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/cr/*
dc.sourceInternational Conference on Ubiquitous Computing and Ambient Intelligence Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information pp 37-49es
dc.subjectInteligencia artificiales
dc.subjectAplicacioneses
dc.subjectDiseñoes
dc.subjectPruebas automatizadases
dc.subjectAutomatizaciónes
dc.subjectResearch Subject Categories::TECHNOLOGY::Information technology::Computer sciencees
dc.titleA top-down design approach for an automated testing frameworkes
dc.typeartículo originales


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