Tipo de publicación: Journal Article
Publicado en: CLEI Electronic Journal
Autores- Abel Méndez-Porras
- Jorge Alfaro-Velásco
- Marcelo Jenkins
- Alexandra Martinez
Investigadores del CITIC asociados a la publicación
Dra. Alexandra Martínez Porras
Dr. Marcelo Jenkins Coronas
Proyecto asociado a la publicación
Proyecto sombrilla
- Bug analyzer
- Image processing
- Interest points
- Testing
- User-interaction features
Context: Mobile applications support a set of user-interaction features that are inde- pendent 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 user- interaction 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.