  Translating a program written in one programming language to another can be
useful for software development tasks that need functionality implementations
in different languages. Although past studies have considered this problem,
they may be either specific to the language grammars, or specific to certain
kinds of code elements (e.g., tokens, phrases, API uses). This paper proposes a
new approach to automatically learn cross-language representations for various
kinds of structural code elements that may be used for program translation. Our
key idea is two folded: First, we normalize and enrich code token streams with
additional structural and semantic information, and train cross-language vector
representations for the tokens (a.k.a. shared embeddings based on word2vec, a
neural-network-based technique for producing word embeddings; Second,
hierarchically from bottom up, we construct shared embeddings for code elements
of higher levels of granularity (e.g., expressions, statements, methods) from
the embeddings for their constituents, and then build mappings among code
elements across languages based on similarities among embeddings.
  Our preliminary evaluations on about 40,000 Java and C# source files from 9
software projects show that our approach can automatically learn shared
embeddings for various code elements in different languages and identify their
cross-language mappings with reasonable Mean Average Precision scores. When
compared with an existing tool for mapping library API methods, our approach
identifies many more mappings accurately. The mapping results and code can be
accessed at
https://github.com/bdqnghi/hierarchical-programming-language-mapping. We
believe that our idea for learning cross-language vector representations with
code structural information can be a useful step towards automated program
translation.

Machine Learning;Computation and Language;Software Engineering