Google llc (20240184555). ITERATIVE NEURAL CODE TRANSLATION simplified abstract

ITERATIVE NEURAL CODE TRANSLATION

Organization Name

google llc

Inventor(s)

Giovanni De Toni of Recoaro Terme (IT)

Rishabh Singh of San Jose CA (US)

Jonathan Malmaud of Campbell CA (US)

Navneet Potti of Sunnyvale CA (US)

ITERATIVE NEURAL CODE TRANSLATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240184555 titled 'ITERATIVE NEURAL CODE TRANSLATION

Simplified Explanation

The techniques described in this patent application involve iterative code generation using neural language models. Here is a simplified explanation of the abstract:

• Original source code is translated from one programming language to another using a machine learning model.
• Errors in the initial translation are identified and corrected by inserting masks.
• The corrected code is then translated back into the second programming language.

1. 1. 1. Potential Applications

The technology can be applied in software development tools to automatically convert code between different programming languages.

1. 1. 1. Problems Solved

This technology helps in reducing the manual effort required to translate code between programming languages, thus saving time and improving efficiency.

1. 1. 1. Benefits

The benefits of this technology include faster code translation, improved accuracy, and increased productivity for developers.

1. 1. 1. Potential Commercial Applications

This technology can be used in integrated development environments (IDEs) and code editors to provide real-time code translation capabilities.

1. 1. 1. Possible Prior Art

One possible prior art for this technology could be the use of rule-based translation systems for converting code between programming languages.

1. 1. 1. 1. What are the limitations of this technology?

One limitation of this technology could be the accuracy of the machine learning model in identifying and correcting errors in the code translation process.

1. 1. 1. 1. How does this technology compare to traditional code translation methods?

This technology offers a more automated and efficient approach to code translation compared to traditional manual methods, which can be time-consuming and error-prone.

1. 1. 1. Frequently Updated Research

One frequently updated research area related to this technology could be the development of more advanced neural language models for code translation.

Original Abstract Submitted

techniques are described herein for iterative code generation using neural language models. in various implementations, an original source code snippet in a first programming language may be processed using a translation machine learning model to generate a first translation of the original source code snippet in a second programming language. the first translation of the original source code snippet may be evaluated to identify error(s) in the first translation. based on the error(s), respective mask(s) may be inserted to generate a masked first translation of the original source code snippet in the second programming language. the masked first translation of the original source code snippet may be processed using the translation machine learning model to generate a second translation of the original source code snippet in the second language. the second translation may include infill(s) of corrected source code in place of one or more of the masks.