Recommending APIs for Mashup Completion Using Association Rules Mined from Real Usage Data

Abstract

Mashups are becoming the de facto approach to build customer-oriented Web applications, by combining several Web APIs into a single lightweight, rich, customized Web front-end. To help mashup builders to choose among a plethora of available APIs to assemble in their mashups, some existing recommendation techniques rank candidate APIs using popularity (a social measure) or keyword-based measures (whether semantic or unverified tags). This article proposes to use information on co-usage of APIs in previous mash ups to suggest likely candidate APIs, and introduces a global measure which improves on earlier local co-API measures. The gCAR (global Co-utilization API Ranking) is calculated using association rules inferred from historical API usage data. The MashupRECO tool combines gCAR and a keywordbased measure, to avoid the "cold-start" problem for new or unused APIs. Evaluation of MashupRECO versus the keyword search of the well-known ProgrammableWeb catalog show that the tool reduces the search time for comparable degree of completeness.