Inferring web API descriptions from usage data
We describe a set of techniques to infer structured descriptions of web APIs from usage examples. Using trained classifiers, we identify fixed and variable segments in paths, and tag parameters according to their types. We implemented our techniques and evaluated their precision on 10 APIs for which we obtained: 1) descriptions, manually written by the API maintainers, and 2) server logs of the API usage. Our experiments show that our system is able to reconstruct the structure of both simple and complex web API descriptions, outperforming an existing tool with similar goals. Finally, we assess the impact of noise in the input data on the results of our method.