Multi-Objective Improvement of Android Applications
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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 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
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