Callan, James; Langdon, William; Petke, Justyna; (2024) On Reducing Network Usage with Genetic Improvement. In: Proceedings of GI@ICSE 2024: 13th International Workshop. Association for Computing Machinery (ACM): Lisbon, Portugal.

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Abstract

Mobile applications can be very network-intensive. Mobile phone users are often on limited data plans, while network infrastructure has limited capacity. There’s little work on optimizing network usage of mobile applications. The most popular approach has been prefetching and caching assets. However, past work has shown that developers can improve the network usage of Android applications by making changes to Java source code. We built upon this insight and investigated the effectiveness of automated, heuristic application of software patches, a technique known as Genetic Improvement (GI), to improve network usage. Genetic improvement has already shown effective at reducing the execution time and memory usage of Android applications. We thus adapt our existing GIDroid framework with a new mutation operator and develop a new profiler to identify network-intensive methods to target. Unfortunately, our approach is unable to find improvements. We conjecture this is due to the fact source code changes affecting network might be rare in the large patch search space. We thus advocate use of more intelligent search strategies in future work.

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