A User Interaction Bug Analyzer based on Imaging Processing
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 pr...
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| Format: | Article |
| Language: | English |
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Centro Latinoamericano de Estudios en Informática
2016-08-01
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| Series: | CLEI Electronic Journal |
| Subjects: | |
| Online Access: | http://www.clei.org/cleiej-beta/index.php/cleiej/article/view/418 |
| _version_ | 1819029872421371904 |
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| author | Abel Méndez-Porras Jorge Alfaro-Velasco Marcelo Jenkins Alexandra Martínez Porras |
| author_facet | Abel Méndez-Porras Jorge Alfaro-Velasco Marcelo Jenkins Alexandra Martínez Porras |
| author_sort | Abel Méndez-Porras |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T06:21:10Z |
| format | Article |
| id | doaj.art-bec95e6a98ce40ff8ef46c1bb1d8192a |
| institution | Directory Open Access Journal |
| issn | 0717-5000 |
| language | English |
| last_indexed | 2024-12-21T06:21:10Z |
| publishDate | 2016-08-01 |
| publisher | Centro Latinoamericano de Estudios en Informática |
| record_format | Article |
| series | CLEI Electronic Journal |
| spelling | doaj.art-bec95e6a98ce40ff8ef46c1bb1d8192a2022-12-21T19:13:14ZengCentro Latinoamericano de Estudios en InformáticaCLEI Electronic Journal0717-50002016-08-0119210.19153/cleiej.19.2.3A User Interaction Bug Analyzer based on Imaging ProcessingAbel Méndez-Porras0Jorge Alfaro-Velasco1Marcelo Jenkins2Alexandra Martínez Porras3University of Costa Rica, Computer and Information Science Graduate Program, Montes de Oca, Costa RicaCosta Rica Institute of Technology, Department of Computer Science, Ciudad Quesada, Costa RicaUniversity of Costa Rica, Computer and Information Science Department, Montes de Oca, Costa RicaUniversity of Costa Rica, Computer and Information Science Department, Montes de Oca, Costa RicaContext: 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.http://www.clei.org/cleiej-beta/index.php/cleiej/article/view/418bug analyzeruser-interaction featuresimage processinginterest pointstesting |
| spellingShingle | Abel Méndez-Porras Jorge Alfaro-Velasco Marcelo Jenkins Alexandra Martínez Porras A User Interaction Bug Analyzer based on Imaging Processing CLEI Electronic Journal bug analyzer user-interaction features image processing interest points testing |
| title | A User Interaction Bug Analyzer based on Imaging Processing |
| title_full | A User Interaction Bug Analyzer based on Imaging Processing |
| title_fullStr | A User Interaction Bug Analyzer based on Imaging Processing |
| title_full_unstemmed | A User Interaction Bug Analyzer based on Imaging Processing |
| title_short | A User Interaction Bug Analyzer based on Imaging Processing |
| title_sort | user interaction bug analyzer based on imaging processing |
| topic | bug analyzer user-interaction features image processing interest points testing |
| url | http://www.clei.org/cleiej-beta/index.php/cleiej/article/view/418 |
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