Improving Web API discovery by leveraging social information

A common problem that mashup developers face is the discovery of APIs that suit their needs. This primary task becomes harder, tedious and time-consuming with the proliferation of new APIs. As humans, we learn by example, following community previous decisions when creating mashups. Most techniques do not consider at all reusing this social information. In this paper, we propose to combine current discovery techniques (exploration) with social information (exploitation). Our preliminary results show that by considering the reciprocal influence of both sources, the discovery process reveals APIs that would remain with low rank because the preferential attachment (popularity) and/or the lack of better descriptions (discovery techniques). We present a case study focusing on a public Web-based API registry.