  Non-functional properties, such as runtime or memory use, are important to
mobile app users and developers, as they affect user experience. Previous work
on automated improvement of non-functional properties in mobile apps failed to
address the inherent trade-offs between such properties. We propose a practical
approach and the first open-source tool, GIDroid (2023), for multi-objective
automated improvement of Android apps. In particular, we use Genetic
improvement, a search-based technique that navigates the space of software
variants to find improved software. We use a simulation-based testing framework
to greatly improve the speed of search. GIDroid contains three state-of-the-art
multi-objective algorithms, and two new mutation operators, which cache the
results of method calls. Genetic improvement relies on testing to validate
patches. Previous work showed that tests in open-source Android applications
are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a
new benchmark for performance improvements. We used GIDroid to improve versions
of mobile apps where developers had previously found improvements to runtime,
memory, and bandwidth use. Our technique automatically re-discovers 64% of
existing improvements. We then applied our approach to current versions of
software in which there were no known improvements. We were able to improve
execution time by up to 35%, and memory use by up to 33% in these apps.
