A distributed bug analyzer based on user-interaction features for mobile apps

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 ba...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2017-08, Vol.8 (4), p.579-591
Main Authors: Méndez-Porras, Abel, Méndez-Marín, Giovanni, Tablada-Rojas, Alberto, Hidalgo, Mario Nieto, García-Chamizo, Juan Manuel, Jenkins, Marcelo, Martínez, Alexandra
Format: Article
Language:English
Subjects:
Applications programs
Artificial Intelligence
Automation
Computational Intelligence
Computers
Digital imaging
Engineering
Mobile computing
Original Research
Robotics and Automation
Sensors
Similarity
Software
Software development
Software testing
User interface
User Interfaces and Human Computer Interaction
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Summary: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.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-016-0435-7