API recommendation for the development of Android App features based on the knowledge mined from App stores

Shanquan Gao; Lei Liu; Yuzhou Liu; Huaxiao Liu; Yihui Wang

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API recommendation for the development of Android App features based on the knowledge mined from App stores

机译：基于App Store中挖掘的知识的Android应用程序功能开发的API建议

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摘要

To improve the efficiency, developers tend to use APIs to avoid reinventing wheels in the development of Apps. However, there are thousands of APIs for various purposes, so it is difficult for developers to identify suitable APIs according to the functionalities to be realized. App stores manage millions of products, which embody the experience and wisdom of developers, and they provide valuable data resource for solving this problem. By summarizing the API usage for the same or similar functionalities in Apps, reusable knowledge can be mined for the API recommendation. In this paper, we utilize the data resource in App stores and provide an API recommendation method for the development of Android Apps. Firstly, by using UI elements as the bridge, we establish mapping relationships between functionalities and APIs. Secondly, we build a framework to describe functionalities of Apps in the same category, and utilize relationships between functionalities and APIs to construct the API knowledge for each node in the framework. Thirdly, we identify nodes according to queried features and show the API knowledge to developers by giving recommendation lists. We conducted experiments based on Google Play, and the result shows that our method has a good recommendation performance.

机译：为了提高效率，开发人员倾向于使用API来避免在应用程序的开发中重新打造轮子。然而，有数千个API用于各种目的，因此开发人员难以根据要实现的功能来识别合适的API。 App Stores管理数百万产品，这些产品体现了开发人员的经验和智慧，并为解决此问题提供有价值的数据资源。通过总结应用程序中相同或相似功能的API使用，可以为API推荐开设可重复使用的知识。在本文中，我们利用App商店中的数据资源，并为Android应用程序提供API推荐方法。首先，通过使用UI元素作为桥梁，我们建立了功能和API之间的映射关系。其次，我们构建一个框架来描述相同类别中应用程序的功能，并利用功能和API之间的关系来构建框架中每个节点的API知识。第三，我们通过询问功能来识别节点，并通过为开发人员提供推荐列表来显示API知识。我们对基于Google Play进行了实验，结果表明我们的方法具有良好的推荐性能。

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来源
《Science of Computer Programming》 |2021年第1期|102556.1-102556.18|共18页
作者
Shanquan Gao; Lei Liu; Yuzhou Liu; Huaxiao Liu; Yihui Wang;
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作者单位

College of Computer Science and Technology Jilin University. Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China;

College of Computer Science and Technology Jilin University. Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China;

College of Computer Science and Technology Jilin University. Changchun 130012 China College of Electronic Science and Engineering Jilin University Changchun China;

College of Computer Science and Technology Jilin University. Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China;

College of Computer Science and Technology Jilin University. Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
API recommendation; App store mining; UI analysis; Reusable knowledge; Feature extraction;

机译：API推荐;App Store Mining;UI分析;可重复使用的知识;特征提取;

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API recommendation for the development of Android App features based on the knowledge mined from App stores

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