TMAP: Discovering relevant API methods through text mining of API documentation

摘要

Developers often migrate their applications to support various platform/programming-languagernapplication programming interfaces (APIs) to retain existing users and to attract new users. Tornmigrate an applicationwritten using1API (source) to anotherAPI (target), a developermust knowrnhowthe methods in the sourceAPImapto the methods in the targetAPI.Given that a typical platformrnor language exposes a large number of API methods, manually discovering API mappings isrnprohibitively resource-intensive and may be error prone. The goal of this research is to supportrnsoftware developers in migrating an application from a source API to a target API by automaticallyrndiscovering relevant method mappings acrossAPIs using text mining on the natural languagernAPI method descriptions. This paper proposes text mining based approach (TMAP) to discoverrnrelevant API mappings. To evaluate our approach, we used TMAP to discover API mappings forrn15 classes across (1) Java and C# API; and (2) Java ME and Android API. We compared therndiscovered mappings with state-of-the-art source code analysis-based approaches: Rosetta andrnStaMiner. Our results indicate that TMAP on average found relevant mappings for 56% and 57%rnmore methods compared to the Rosetta and the StaMiner approaches, respectively.