论文标题

指导软件开发人员通过社交网络应用程序插件使用多个桥梁源存储库通过数据挖掘进行集成方法

Guiding Software Developers by Social Networking Application Plug-in using the Multiple Bridge Source Repository through a Data Mining Integrated Approach

论文作者

Corraya, Anjela Diana, Sumi, Mousumi Akter, Shachi, Sadia Islam, Rahman, Ziaur

论文摘要

在当今的世界中,社交网络是大众传播的重要(力量完整)媒介。几乎所有班级的人都通过在不同的社交网站中使用庞大的用户友好应用程序来互动,并分享他们的观点,时刻和想法。找到一个从未听说过社交网络的人真是令人难以置信。可用的社交网站通常适合其用户通过特定模板和嵌入式代码来源开发各种自定义应用程序。具有平均开发知识的用户通常会遇到难以重复这些资源,并最终缺乏准则和必要的API建议。在我们的工作中,我们提出了一个框架和模型,以通过用户帮助插件工具来帮助这些应用程序开发人员,该工具能够根据特定的用户查询提供相同的API使用模式和序列。我们将系统标题为社交网络应用程序插件(SNAP)。我们搜索社交网络应用程序存储库,其中桥接了多个存储,并应用各自的采矿算法来找到相关的序列以满足用户需求。它提供了类似,最相关和功能性的API用法方案,并提供了选择,重复使用和修改推荐来源的选项。从我们做过的调查中,我们的快照方法能够推荐用户无错误,易于理解和最小的API模式。

In today's world, social networking is an important (power full) medium of mass communication. People of almost all classes have been interacting with each other and sharing their views, moments, and ideas by using enormous user-friendly applications in different social networking sites. It's really unbelievable to find a person who never heard about the social network. The available social networking sites usually opportune their users to develop various customized applications through particular templates and embedded sources of codes. The users with average knowledge of development often encounter difficulties to reuse those resources and eventually lack guidelines and necessary API recommendations. In our work, we have proposed a framework and model to help those apps developers through a user assistance plug-in tool that is able to provide identical API usage patterns and sequences in response to a particular user query. We have titled our system as a Social Networking Application Plug-in (SNAP). We search social networking apps repository where multiple storages are bridged and apply respective mining algorithm to find the relevant sequences to fulfill the user needs. It provides similar, most relevant, and functional API usage scenarios as well as gives an option to choose, reuse, and modify the recommended sources. From investigations we have ever made, our SNAP approach is capable to recommend users' error-free, understandable, and minimal API patterns.

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