TY - GEN
T1 - A novel approach for API recommendation in mashup development
AU - Li, Chune
AU - Zhang, Richong
AU - Huai, Jinpeng
AU - Sun, Hailong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - API recommendation
KW - Mashup development
KW - Relational topic model
UR - https://www.scopus.com/pages/publications/84926217790
U2 - 10.1109/ICWS.2014.50
DO - 10.1109/ICWS.2014.50
M3 - 会议稿件
AN - SCOPUS:84926217790
T3 - Proceedings - 2014 IEEE International Conference on Web Services, ICWS 2014
SP - 289
EP - 296
BT - Proceedings - 2014 IEEE International Conference on Web Services, ICWS 2014
A2 - De Roure, David
A2 - Thuraisingham, Bhavani
A2 - Zhang, Jia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 21st IEEE International Conference on Web Services, ICWS 2014
Y2 - 27 June 2014 through 2 July 2014
ER -