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).
1.2 KiB
category, type, person, date, source
| category | type | person | date | source |
|---|---|---|---|---|
| academic | academic | Yanxin Lu | 2013 | gokhale_13_infer.pdf |
Inferring Likely Mappings between APIs
Amruta Gokhale, Vinod Ganapathy, Yogesh Padmanaban (Rutgers University)
ICSE 2013
Software developers often need to port applications written for a source platform to a target platform. A key task is to replace the source platform API with corresponding methods from the target platform API. This paper develops a novel approach to the problem of inferring likely mappings between the APIs of a source and target platform. The approach is tailored to the case where the source and target platform each have independently-developed applications that implement similar functionality. The authors observe that in building these applications, developers exercised knowledge of the corresponding APIs, and develop a technique to systematically harvest this knowledge and infer likely mappings between the APIs. The output is a ranked list of target API methods or method sequences that likely map to each source API method or method sequence. The prototype tool Rosetta was applied to infer likely mappings between the Java2 Platform Mobile Edition and Android graphics APIs.