Hierarchical learning of cross-language mappings through distributed vector representations for code
Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2018
Abstract
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.
Keywords
Language mapping, Program translation, Software maintenance, Syntactic structure, Word2vec
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ICSE-NIER '18: Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results: Gothenburg, Sweden, May 30 - June 3
First Page
33
Last Page
36
ISBN
9781450356626
Identifier
10.1145/3183399.3183427
Publisher
ACM
City or Country
New York
Citation
BUI, Nghi D. Q. and JIANG, Lingxiao.
Hierarchical learning of cross-language mappings through distributed vector representations for code. (2018). ICSE-NIER '18: Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results: Gothenburg, Sweden, May 30 - June 3. 33-36.
Available at: https://ink.library.smu.edu.sg/sis_research/4090
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3183399.3183427