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Under the fierce competition and budget constraints, most mobile apps are launched without sufficient tests. Thus, there exists a great demand for automated app testing. Recent developments in various machine learning techniques have made automated app testing a promising alternative to manual testing. This work proposes novel approaches for one of the core functionalities of automated app testing: the detection of changes in usage-phases of a mobile app. Because of the flexibility of app<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app10103656">doi:10.3390/app10103656</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/us5frw3hwncplj7gfhli7pgxze">fatcat:us5frw3hwncplj7gfhli7pgxze</a> </span>
more »... pment languages and the lack of standards, each mobile app is very different from other apps. Furthermore, the graphical user interfaces for similar functionalities are rarely consistent or similar. Thus, we propose methods detecting usage-phase changes through object recognition and metrics utilizing graphs and generative models. Contrary to the existing change detection methods requiring learning models, the proposed methods eliminate the burden of training models. This elimination of training is suitable for mobile app testing whose typical usage-phase is composed of less than 10 screenshots. Our experimental results on commercial mobile apps show promising improvement over the state-of-the-practice method based on SIFT (scale-invariant feature transform).
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