Context-Aware API Recommendation for Mashup Composition Based on Path Ranking

In recent years, more software modules have been increasingly published onto the Internet as web APIs, making the activity of manually composing mashups time-consuming and error-prone for mashup developers. In both academia and industry, discovering and recommending suitable web APIs has become a core method to increase the working efficiency for mashup developers. In this paper, we propose a novel context-aware approach based on path ranking algorithm (PRA) to recommending next potential APIs in a recommend-as-you-go manner. At every step throughout the process of composing a mashup, the APIs that have been selected and the collection of words in the textually described requirement are regarded as contextual input of a prediction model which is comprised of weights of various relation paths being built from a relation schema. Given the input query, candidate APIs are ranked in descending order according to its predicted score of being the API that should be selected at next step. Finally, top K of these candidate APIs are recommended to the mashup developer. Extensive experiments on a real-world dataset demonstrate that our approach is effective.