Learning to Infer API Mappings from API Documents

To satisfy business requirements of various platforms and devices, developers often need to migrate software code from one platform to another. During this process, a key task is to figure out API mappings between API libraries of the source and target platforms. Since doing it manually is time-consuming and error-prone, several code-based approaches have been proposed. However, they often have the issues of availability on parallel code bases and time expense caused by static or dynamic code analysis. In this paper, we present a document-based approach to infer API mappings. We first learn to understand the semantics of API names and descriptions in API documents by a word embedding model. Then we combine the word embeddings with a text similarity algorithm to compute semantic similarities between APIs of the source and target API libraries. Finally, we infer API mappings from the ranking results of API similarities. Our approach is evaluated on API documents of JavaSE and .NET. The results outperform the baseline model at precision@k by 41.51% averagely. Compared with code-based work, our approach avoids their issues and leverages easily acquired API documents to infer API mappings effectively.