ff146a9362
Reference papers on API mapping, migration, and evolution collected during PhD research (2018). Topics include: API usage adaptation (LibSync), statistical API mapping mining (StaMiner, MAM), API mapping via vector representations (Word2Vec), text mining for API mappings (TMAP), library migration graphs, framework evolution (AURA), class library migration refactoring, and API specification inference (Doc2Spec).
996 B
category, type, person, date, source
| category | type | person | date | source |
|---|---|---|---|---|
| academic | academic | Yanxin Lu | 2015 | 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.