Multi-Objective Improvement of Android Applications

TL;DR

This paper introduces GIDroid, an open-source tool that uses genetic improvement and multi-objective algorithms to optimize non-functional properties of Android apps, effectively balancing trade-offs like runtime and memory use.

Contribution

It presents the first practical, open-source multi-objective optimization tool for Android apps, incorporating novel mutation operators and a simulation-based testing framework.

Findings

GIDroid re-discovers 64% of known improvements in app performance.

It improves execution time by up to 35% in current app versions.

Memory usage is reduced by up to 33% in tested apps.

Abstract

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.