Recommending API Usages for Mobile Apps with Hidden Markov Model
Recommending API Usages for Mobile Apps with Hidden Markov Model
复制标题
DOI：
10.1109/ase.2015.109
复制
发表时间：
2015-11
期刊：
影响因子：
--
通讯作者：
Tam The Nguyen;H. Pham;P. Vu;T. Nguyen
中科院分区：
文献类型：
--
作者：
Tam The Nguyen;H. Pham;P. Vu;T. Nguyen
移动的应用程序通常严重依赖标准API框架和库。然而，由于API框架的快速变化特性以及文档和代码示例的不足，学习使用这些API通常具有挑战性。本文介绍了DroidAssist，这是一个针对Android移动的应用程序的API用法的推荐工具。DroidAssist的核心是HAPI，这是一种基于隐马尔可夫模型的API使用情况的统计生成模型。通过从现有的移动的应用程序中训练HAPI，DroidAssist可以为方法调用执行代码完成。它还可以检查现有的调用序列，以检测和修复可疑的（即不受欢迎的）API使用。
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.