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Computer programs written in one language are often required to be ported toother languages to support multiple devices and environments. When programs uselanguage specific APIs (Application Programming Interfaces), it is verychallenging to migrate these APIs to the corresponding APIs written in otherlanguages. Existing approaches mine API mappings from projects that havecorresponding versions in two languages. They rely on the sparse availabilityof bilingual projects, thus producing a limited number of API mappings. In thispaper, we propose an intelligent system called DeepAM for automatically miningAPI mappings from a large-scale code corpus without bilingual projects. The keycomponent of DeepAM is based on the multimodal sequence to sequence learningarchitecture that aims to learn joint semantic representations of bilingual APIsequences from big source code data. Experimental results indicate that DeepAMsignificantly increases the accuracy of API mappings as well as the number ofAPI mappings, when compared with the state-of-the-art approaches.

DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning