---
category: academic
type: academic
person: Yanxin Lu
date: 2015
source: pandita_15_text.pdf
---

# Discovering Likely Mappings between APIs using Text Mining

Rahul Pandita (NC State), Raoul Praful Jetley, Sithu D Sudarsan (ABB Corporate Research), Laurie Williams (NC State)

SCAM 2015

Developers often release different versions of their applications to support various platform/programming-language APIs. This paper proposes TMAP: Text Mining based approach to discover likely API method mappings using the similarity in the textual description of the source and target API documents. TMAP uses a vector space model of target API method descriptions, then queries it with automatically generated queries from the source API. Results show TMAP on average found relevant mappings for 57% more methods compared to previous approaches (Rosetta and StaMiner), and on average found exact mappings for 6.5 more methods per class with a maximum of 21 additional exact mappings for a single class.