A Bayesian Learning Model for Design-Phase Service Mashup Popularity Prediction

Abstract Using web services as building blocks to develop software applications, i.e., service mashups, not only reuses software development efforts to minimize development cost, but also leverages user groups and marketing efforts of those services to attract users and improve profits. This has significantly encouraged the development of a large number of service mashups in various domains. However, using existing services, even popular ones, does not guarantee the success of a mashup. In fact, a large portion of existing mashups fail to attract a good number of users, making the mashup development effort less effective. Design-phase popularity prediction can help avoid unpromising mashup developments by providing early-on insight into the potential popularity of a mashup. In this paper, we investigate the factors that can affect the popularity of a mashup through a comprehensive analysis on one of the largest mashup repository (i.e., ProgrammableWeb). We further propose a novel Bayesian approach that offers early-on insight to developers into the potential popularity of a mashup using design-phase features only. Besides identifying those relevant features, the Bayesian learning model can provide a confidence level for each prediction. This provides useful guidance to developers for successful mashup development. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and outperforms competitive models.