Recommending API Usages for Mobile Apps with Hidden Markov Model

Abstract

Mobile apps often rely heavily on standard API frameworks and libraries. However, learning to use those APIs is often challenging due to the fast-changing nature of API frameworks and the insufficiency of documentation and code examples. This paper introduces DroidAssist, a recommendation tool for API usages of Android mobile apps. The core of DroidAssist is HAPI, a statistical, generative model of API usages based on Hidden Markov Model. With HAPIs trained from existing mobile apps, DroidAssist could perform code completion for method calls. It can also check existing call sequences to detect and repair suspicious (i.e. unpopular) API usages.