Assisting end-user development in browser-based mashup tools

Despite the recent progresses in end-user development and particularly in mashup application development, developing even simple mashups is still non-trivial and requires intimate knowledge about the functionality of web APIs and services, their interfaces, parameter settings, data mappings, and so on. We aim to assist less skilled developers in composing own mashups by interactively recommending composition knowledge in the form of modeling patterns and fostering knowledge reuse. Our prototype system demonstrates our idea of interactive recommendation and automated pattern weaving, which involves recommending relevant composition patterns to the users during development, and once selected, applying automatically the changes as suggested in the selected pattern to the mashup model under development. The experimental evaluation of our prototype implementation demonstrates that even complex composition patterns can be efficiently stored, queried and weaved into the model under development in browser-based mashup tools.