A Novel Approach for API Recommendation in Mashup Development
A Novel Approach for API Recommendation in Mashup Development
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DOI：
10.1109/icws.2014.50
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Chune Li;Richong Zhang;J. Huai;Hailong Sun
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文献类型：
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作者：
Chune Li;Richong Zhang;J. Huai;Hailong Sun
混合 Web 服务和 RESTful API 是一种开发新应用程序的新颖编程方法。随着可用资源数量的迅速增加，发现潜在的服务或 API 变得越来越困难。因此，减轻混搭开发人员的服务发现负担至关重要。在本文中，我们提出了一种概率模型，通过推荐可用于在给定混搭描述的情况下组成所需混搭的 API 列表来帮助混搭创建者。具体来说，利用关系主题模型来表征混搭、API 及其链接之间的关系。此外，我们将 API 的流行度纳入模型中，并对混搭和 API 之间的链接进行预测。此外，通过对公共mashup平台的统计分析，展示了mashup的发展现状以及本研究的适用性。对大型服务数据集的实验证实了该方法的有效性。
Mashing up Web services and RESTful APIs is a novel programming approach to develop new applications. As the number of available resources is increasing rapidly, to discover potential services or APIs is getting difficult. Therefore, it is vital to relieve mashup developers of the burden of service discovery. In this paper, we propose a probabilistic model to assist mashup creators by recommending a list of APIs that may be used to compose a required mashup given descriptions of the mashup. Specifically, a relational topic model is exploited to characterize the relationship among mashups, APIs and their links. In addition, we incorporate the popularity of APIs to the model and make predictions on the links between mashups and APIs. Moreover, the statistical analysis on a public mashup platform shows the current status of mashup development and the applicability of this study. Experiments on a large service data set confirm the effectiveness of this proposed approach.