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.
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Acknowledgements
This research was supported by the Costa Rican Ministry of Science, Technology and Telecommunications (MICITT). We also thank the Computer and Information Science Department as well as the Graduate Program in Computer and Information Science from the University of Costa Rica.
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Méndez-Porras, A., Méndez-Marín, G., Tablada-Rojas, A. et al. A distributed bug analyzer based on user-interaction features for mobile apps. J Ambient Intell Human Comput 8, 579–591 (2017). https://doi.org/10.1007/s12652-016-0435-7
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DOI: https://doi.org/10.1007/s12652-016-0435-7