Simplifying mashup component selection with a combined similarity- and social-based technique

Web mashups are becoming the main approach to build Web applications. Current approaches to enable component selection include description-based techniques and socially generated metadata. The explosive growth of APIs makes increasingly harder selecting appropriate components for each mashup. Unfortunately, description-based techniques rely heavily on the quality of authors' information, and social-based approaches suffer problems like "cold-start" and "preferential attachment". This article proposes (1) two new measures of socially ranked fitness of candidate components, (2) an API functional taxonomy using Formal Concept Analysis based on descriptions, and (3) a combined approach that improves description-based techniques with these social ranking measures. We use social rankings based on past (co-)utilization of APIs: WAR (Web API Rank) measures API utilization over time, and CAR (Co-utilization API Rank) measures its co-utilization with other APIs. The measures and the combined approach are illustrated with a case study using the well-known Web APIs catalog ProgrammableWeb ¹. A prototype tool allows iterative discovery of APIs and assists the mashup creation process.