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 | 2014 | nguyen_14_staminer.pdf |
Statistical Learning Approach for Mining API Usage Mappings for Code Migration
Anh Tuan Nguyen, Hoan Anh Nguyen, Tung Thanh Nguyen, Tien N. Nguyen (Iowa State University, Utah State University)
ASE 2014
The same software product nowadays could appear in multiple platforms and devices. To address business needs, software companies develop a product in one language and then migrate it to another. The authors introduce StaMiner, a novel data-driven approach that statistically learns the mappings between APIs from the corpus of the corresponding client code of the APIs in two languages Java and C#. Instead of using heuristics on textual or structural similarity to map API methods and classes, StaMiner is based on a statistical model that learns the mappings from a corpus and provides mappings for APIs with all possible arities. Empirical evaluation shows StaMiner can detect API usage mappings with higher accuracy than state-of-the-art approaches. With the resulting API mappings mined by StaMiner, Java2CSharp, an existing migration tool, could achieve a higher level of accuracy.