The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 18, 2025

Filed:

Jul. 24, 2022
Applicant:

Fujitsu Limited, Kanagawa, JP;

Inventors:

Mehdi Bahrami, San Jose, CA (US);

Wei-Peng Chen, Fremont, CA (US);

Assignee:

Fujitsu Limited, Kawasaki, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/445 (2018.01); G06F 8/30 (2018.01); G06F 8/36 (2018.01); G06F 8/41 (2018.01); G06F 8/65 (2018.01); G06F 8/73 (2018.01); G06F 11/362 (2025.01); G06F 16/951 (2019.01); G06F 18/20 (2023.01); G06F 18/22 (2023.01); G06F 18/23213 (2023.01); G06F 40/166 (2020.01); G06F 40/211 (2020.01); G06F 40/216 (2020.01); G06F 40/242 (2020.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06F 40/44 (2020.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 8/30 (2013.01); G06F 8/36 (2013.01); G06F 8/42 (2013.01); G06F 8/436 (2013.01); G06F 8/65 (2013.01); G06F 8/73 (2013.01); G06F 11/3624 (2013.01); G06F 16/951 (2019.01); G06F 18/22 (2023.01); G06F 18/23213 (2023.01); G06F 18/285 (2023.01); G06F 40/166 (2020.01); G06F 40/211 (2020.01); G06F 40/216 (2020.01); G06F 40/242 (2020.01); G06F 40/40 (2020.01); G06F 40/44 (2020.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01);
Abstract

According to an aspect of an embodiment, operations for deep parameter learning for code synthesis are provided. The operations may include receiving a source code file and generating an abstract syntax tree (AST). The operations may further include determining a set of classes, and functions/procedures from the computer-executable code and extracting metadata associated to each component. The operations may further include selecting a subset of functions for which descriptions in the extracted metadata satisfy filtering criteria and updating the computer-executable code by filtering lines of code (LoCs) corresponding to the subset of functions/procedures. The operations may further include generating a dataset of code features and respective metadata features that includes a deep connection between parameters and its usage based on the updated computer-executable code and the metadata generation task. The operations may further include training a language model on a sequence-to-sequence generation task, based on the dataset.


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