View source for Google llc (20240111497). Augmentation of Code Completion and Code Synthesis with Semantic Checking simplified abstract

=Augmentation of Code Completion and Code Synthesis with Semantic Checking=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:Maxim Tabachnyk of Munich (DE)|Maxim Tabachnyk of Munich (DE)]][[Category:Maxim Tabachnyk of Munich (DE)]]

[[:Category:Yurun Shen of Mountain View CA (US)|Yurun Shen of Mountain View CA (US)]][[Category:Yurun Shen of Mountain View CA (US)]]

[[:Category:Stoyan Stefanov Nikolov of Planegg (DE)|Stoyan Stefanov Nikolov of Planegg (DE)]][[Category:Stoyan Stefanov Nikolov of Planegg (DE)]]

[[:Category:Stanislav Pyatykh of Unterhaching (DE)|Stanislav Pyatykh of Unterhaching (DE)]][[Category:Stanislav Pyatykh of Unterhaching (DE)]]

[[:Category:Ksenia Korovina of Mountain View CA (US)|Ksenia Korovina of Mountain View CA (US)]][[Category:Ksenia Korovina of Mountain View CA (US)]]

[[:Category:Evgeny Gryaznov of Mountain View CA (US)|Evgeny Gryaznov of Mountain View CA (US)]][[Category:Evgeny Gryaznov of Mountain View CA (US)]]

[[:Category:Erik Grabljevec of Mountain View CA (US)|Erik Grabljevec of Mountain View CA (US)]][[Category:Erik Grabljevec of Mountain View CA (US)]]

==Augmentation of Code Completion and Code Synthesis with Semantic Checking - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240111497 titled 'Augmentation of Code Completion and Code Synthesis with Semantic Checking

==Simplified Explanation==

The abstract describes a method for providing autofill suggestions in a development environment by using machine learning models and rule-based semantic checkers to suggest and validate code completions.

* The method involves obtaining user input representing source code, determining autofill suggestions based on the input using a machine learning model, and checking the suggestions for semantic correctness using a rule-based semantic checker.
* When a suggestion is deemed semantically correct, it is transmitted for display on the user interface of the user device.

== Potential Applications ==
This technology could be applied in various development environments to assist programmers in writing code more efficiently and accurately.

== Problems Solved ==
1. Helps developers save time by providing autofill suggestions for code completion.
2. Ensures the correctness of suggested code completions through semantic checking.

== Benefits ==
1. Improves productivity by speeding up the coding process.
2. Reduces the likelihood of errors in code completion.
3. Enhances the user experience by offering helpful suggestions in real-time.

== Potential Commercial Applications ==
"Enhancing Development Efficiency with Autofill Suggestions in a Development Environment"

== Possible Prior Art ==
There are existing tools and plugins in development environments that offer autofill suggestions for code completion, but the use of machine learning models and rule-based semantic checkers to validate suggestions may be a novel approach.

=== Unanswered Questions ===

=== How does this method handle different programming languages and code bases? ===
The abstract does not specify how the method adapts to various programming languages and code bases to provide accurate autofill suggestions.

=== What is the accuracy rate of the autofill suggestions provided by this method? ===
The abstract does not mention the accuracy rate of the autofill suggestions generated by the machine learning model and semantic checker.


==Original Abstract Submitted==

a method for providing autofill suggestions in a development environment includes obtaining, from a user interface executing on a user device, a user input representing source code generated within a development environment. the source code is created using a particular programming language and a programming code base. the method further includes determining, using a machine learning model, at least one autofill suggestion based on the user input, the autofill suggestion continuing the source code represented by the user input. the method further includes determining, using a rule-based semantic checker configured for the particular programming language, whether the autofill suggestion is semantically correct based on the development environment and the programming code base. the method also includes, when the autofill suggestion is semantically correct, transmitting the autofill suggestion for display on the user interface of the user device.



[[Category:G06F8/33]]