@article {796, title = {A distributed bug analyzer based on user-interaction features for mobile apps}, journal = {Journal of Ambient Intelligence and Humanized Computing}, volume = {8}, year = {2017}, month = {08/2017}, pages = {579{\textendash}591}, abstract = {

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.

}, keywords = {Automated testing, Digital imaging processing, Distributed bug analyzer, Interest points, User-interaction features}, issn = {1868-5145}, doi = {10.1007/s12652-016-0435-7}, url = {https://doi.org/10.1007/s12652-016-0435-7}, author = {M{\'e}ndez-Porras, Abel and M{\'e}ndez-Mar{\'\i}n, Giovanni and Tablada-Rojas, Alberto and Hidalgo, Mario Nieto and Garc{\'\i}a-Chamizo, Juan Manuel and Jenkins, Marcelo and Mart{\'\i}nez, Alexandra} } @article {541, title = {A User-Interaction Bug Analyzer Based on Image Processing}, journal = {CLEI Electronic Journal}, volume = {19}, year = {2016}, month = {08/2016}, abstract = {

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.

}, keywords = {Bug analyzer, Image processing, Interest points, Testing, User-interaction features}, url = {http://www.scielo.edu.uy/pdf/cleiej/v19n2/v19n2a04.pdf}, author = {Abel M{\'e}ndez-Porras and Jorge Alfaro-Vel{\'a}sco and Marcelo Jenkins and Alexandra Martinez} }