NDLI: Time-Aware API Popularity Prediction via Heterogeneous Features

Content Provider	IEEE Xplore Digital Library
Author	Yao Wan Liang Chen Jian Wu Qi Yu
Copyright Year	2015
Description	Author affiliation: Coll. of Comput. & Inf. Sci., Rochester Inst. of Technol., Rochester, NY, USA (Qi Yu) \|\| Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China (Yao Wan; Liang Chen; Jian Wu)
Abstract	Application Programming Interfaces (APIs), which are emerging web services in general, are increasing with a rapid speed in recent years. With so many APIs, many management platforms have been developed and deployed, leading to the boom of API markets, that are similar to the mobile App markets. Meanwhile, it has become more and more difficult to select and manage APIs. In reality, most existing management platforms typically recommend currently popular APIs to developers. However, the fact that popularity of API varies over time is ignored in those platforms, leading to the difficulty of recommending APIs that are just released but may be popular in the near future. To tackle this challenge, an approach of predicting the popularity of APIs is proposed in this paper. Predicting the popularity of API can not only be used for API ranking, recommendation and selection, but also make it more convenient for API providers and consumers to manage or select API respectively. In this paper, we propose a time-aware linear model to predict the API popularity, using time series feature of APIs and API's self-features such as its' provider ranking and description features, which are called heterogeneous features in our paper. Comprehensive experiments have been conducted on a real-world Programmable Web dataset with 613 real APIs. The experimental results show that our model has a better performance, when compared with some other state-of-the-art prediction models.
Starting Page	424
Ending Page	431
File Size	587691
Page Count	8
File Format	PDF
e-ISBN	9781467372725
DOI	10.1109/ICWS.2015.63
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mashups Predictive models Numerical models Time series analysis Quality of service Market research heterogeneous time-aware API popularity prediction
Content Type	Text
Resource Type	Article

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