iPTR: Learning a representation for interactive program translation retrieval

Binger Chen, Ziawasch Abedjan

Abstract: Program translation contributes to many real world scenarios, such as porting codebases written in an obsolete or deprecated language to a modern one or re-implementing existing projects in one's preferred programming language. Existing data-driven approaches either require large amounts of training data or neglect significant characteristics of programs. In this paper, we present iPTR for interactive code translation retrieval from Big Code. iPTR uses a novel code representation technique that encodes structural characteristics of a program and a predictive transformation technique to transform the representation into the target programming language. The transformed representation is used for code retrieval from Big Code. With our succinct representation, the user can easily update and correct the returned results to improve the retrieval process. Our experiments show that iPTR outperforms supervised baselines in terms of program accuracy.

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One-sentence Summary: This paper presents a program translation engine that leverages a novel representation based on structural features of programming languages and pretrained autoencodes to effectively retrieve the best possible translation of a given input program.

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