App testing tools tend to generate thousand test inputs; they help engineers identify crashing conditions but not functional failures. Indeed, detecting functional failures requires the visual inspection of App outputs, which is infeasible for thousands of inputs. Existing App testing tools ignore that most of the Apps are frequently updated and engineers are mainly interested in testing the updated functionalities; indeed, automated regression test cases can be used otherwise. We present ATUA, an open source tool achieving higher coverage of the updated App code with a much smaller number of test inputs, thus alleviating the test oracle problem. It implements a model-based approach that synthesizes App models with static analysis, integrates a dynamically-refined state abstraction function and combines complementary testing strategies, including (1) coverage of the model structure, (2) coverage of the App code, (3) random exploration, and (4) coverage of dependencies identified through information retrieval. Our empirical evaluation, conducted with nine popular Android Apps (72 versions), has shown that ATUA, compared to state-of-the-art approaches, achieves higher code coverage while producing fewer outputs to be manually inspected. A demo video is available at https://youtu.be/RqQ1z_Nkaqo.