  Although App updates are frequent and software engineers would like to verify
updated features only, automated testing techniques verify entire Apps and are
thus wasting resources. We present Continuous Adaptation of Learned Models
(CALM), an automated App testing approach that efficiently test App updates by
adapting App models learned when automatically testing previous App versions.
CALM focuses on functional testing. Since functional correctness can be mainly
verified through the visual inspection of App screens, CALM minimizes the
number of App screens to be visualized by software testers while maximizing the
percentage of updated methods and instructions exercised. Our empirical
evaluation shows that CALM exercises a significantly higher proportion of
updated methods and instructions than six state-of-the-art approaches, for the
same maximum number of App screens to be visually inspected. Further, in common
update scenarios, where only a small fraction of methods are updated, CALM is
even quicker to outperform all competing approaches in a more significant way.
