A user interaction bug analyzer based on image processing
Archivos
Fecha
2016-08
Autores
Méndez Porras, Abel
Alfaro Velásco, Jorge
Jenkins Coronas, Marcelo
Martínez Porras, Alexandra
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Resumen
Mobile applications support a set of user-interaction features that are independent
of the application logic. Rotating the device, scrolling, or zooming are examples
of such features. Some bugs in mobile applications can be attributed to user-interaction
features. Objective: This paper proposes and evaluates a bug analyzer based on userinteraction
features that uses digital image processing to find bugs. Method: Our bug
analyzer detects bugs by comparing the similarity between images taken before and after
a user-interaction. SURF, an interest point detector and descriptor, is used to compare
the images. To evaluate the bug analyzer, we conducted a case study with 15 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 with SURF to obtain interest points, from
which a similarity percentage was computed, to finally decide whether there was a bug.
Results: We performed a total of 49 user-interaction feature tests. When manually testing
the applications, 17 bugs were found, whereas when using image processing, 15 bugs
were detected. Conclusions: 8 out of 15 mobile applications tested had bugs associated
to user-interaction features. Our bug analyzer based on image processing was able to
detect 88% (15 out of 17) of the user-interaction bugs found with manual testing.
Descripción
Palabras clave
Bug analyzer,, User-interaction features, Image processing, Interest points, Testing
Citación
http://www.clei.org/cleiej/paper.php?id=357