@article{discovery10198292,
            year = {2025},
          number = {2},
         journal = {Automated Software Engineering},
       publisher = {Springer Verlag},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           pages = {1--36},
           title = {Multi-Objective Improvement of Android Applications},
          volume = {32},
             url = {https://doi.org/10.1007/s10515-024-00472-7},
        keywords = {Android Apps, Genetic Improvement, Multi-Objective Optimization,
Search-Based Software Engineering},
        abstract = {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.},
          author = {Callan, James and Petke, Justyna}
}