A user interaction bug analyzer based on image processing

Fecha

2016-08

Autores

Méndez Porras, Abel
Alfaro Velásco, Jorge
Jenkins Coronas, Marcelo
Martínez Porras, Alexandra

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ISSN de la revista

Título del volumen

Editor

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