%0 Journal Article
%A Callan, James
%A Petke, Justyna
%D 2025
%F discovery:10198292
%I Springer Verlag
%J Automated Software Engineering
%K Android Apps, Genetic Improvement, Multi-Objective Optimization,  Search-Based Software Engineering
%N 2
%P 1-36
%T Multi-Objective Improvement of Android Applications
%U https://discovery.ucl.ac.uk/id/eprint/10198292/
%V 32
%X Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. We propose a practical approach and the first open-source tool, GIDroid 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.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.