Abstract
Mashups involve the collaboration of multiple developers to build Web applications out of pre-existing APIs. A large body of research focused on recommending APIs for mashups. However, very few contributions looked at recommending developers. In this paper, we propose CrowdMashup, a crowdsourcing approach for mashup teams recommendation. We analyze online developer communities and API directories to infer developers' interests in APIs through natural language processing. We predict missing interest values using the alternating least square method for collaborative filtering. We also model interactions (comments and replies) among developers as a weighted undirected graph and introduce a sociometric to identify socially related developers. We propose an algorithm, based on the concept of cliques in graph theory, that combines developers' skills and sociometric to recommend efficient and balanced teams. We describe a prototype implementation and conduct extensive experiments on real-world data and APIs to evaluate our approach.