US 11,960,867 B1Granted

Using natural language latent representation in automated conversion of source code from base programming language to target programming language

AssigneeGoogle Inc.

InventorsRishabh Singh, Hanjun Dai, Manzil Zaheer, Artem Goncharuk, Karen Davis, David Andre

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Abstract

Using a natural language (NL) latent presentation in the automated conversion of source code from a base programming language (e.g., C++) to a target programming language (e.g., Python). A base-to-NL model can be used to generate an NL latent representation by processing a base source code snippet in the base programming language. Further, an NL-to-target model can be used to generate a target source code snippet in the target programming language (that is functionally equivalent to the base source code snippet), by processing the NL latent representation. In some implementations, output(s) from the NL-to-target model indicate canonical representation(s) of variables, and in generating the target source code snippet, technique(s) are used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated, and a subset (e.g., one) is selected based on evaluation(s).

Claims18

Dependent claims

A method implemented by one or more processors, the method comprising:

identifying a base natural language description that is descriptive of a base source code snippet that is programmed in a base higher-level programming language;
processing the natural language description, using a neural network model, to generate a sequence of outputs that each comprise a corresponding probability distribution;
generating, based on the corresponding probability distributions of second outputs:
a predicted target source code snippet that is in a target higher-level programming language that differs from the base higher-level programming language, and an additional predicted target source code snippet that is in the target higher-level programming language and that includes one or more portions that differ from the predicted target source code snippet;
evaluating the predicted target source code snippet and evaluating the additional predicted target source code snippet;
selecting, based on the evaluations, the additional predicted target source code snippet over the predicted target source code snippet;
and
responsive to selecting the additional predicted target source code snippet:
causing the additional predicted target source code snippet to be rendered as output of a software development application,
wherein the predicted source code snippet is not rendered as output of the software development application.

7 more elements

A method implemented by one or more processors, the method comprising:

identifying, from a repository, a target source code snippet that is programmed in a base programming language;
identifying, from the repository, a base document string for the base source code snippet;
generating multiple training instances based on the base source code snippet and the base document string, wherein each of the training instances differs from all other of the training instances and each includes corresponding training instance input that is based on the base document string and corresponding training instance output that is based on the base source code snippet;
using the training instances to train a neural network model for use in generating natural language based on base source code.

1 more element

Dependencies are inferred from the claim text.

Facts

Application: 18/198,674Filed 17 May 2023 · continuation
Priority: 15 May 2020
Granted: 16 Apr 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06F 8/436
Checking; Contextual analysis: semantic checking
G06F 8/51
Transformation of program code: source to source
G06F 40/279
Natural language analysis: recognition of textual entities
G06F 40/40
Handling natural language data: processing or translation of natural language