Learning Performance Optimization from Code Changes for Android Apps

Abstract

Performance issues of Android apps can tangibly degrade user experience. However, it is challenging for Android developers, especially a novice to develop high-performance apps. It is primarily attributed to the lack of consolidated and abundant programmatic guides for performance optimization. To address this challenge, we propose a data-based approach to obtain performance optimization practices from historical code changes. We first elicit performance-aware Android APIs of which invocations could affect app performance to a large extent, identify historical code changes that produce impact on app performance, and further determine whether they are optimization practices. We have implemented this approach with a tool \tool and evaluated its effectiveness in 2 open source well-maintained projects. The experimental results found 83 changes relevant to performance optimization. Last, we summarize and explain 5 optimization rules to facilitate the development of high-performance apps.