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:
Jun. 25, 2024

Filed:

Mar. 31, 2022
Applicant:

Fujitsu Limited, Kawasaki-shi, 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 8/60 (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 11/36 (2006.01); 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 code enrichment for training language models on tasks related to computer programming are provided. The operations include receiving source code data including a computer-executable code and a natural language (NL) text. The operations further include determining blocks of code from the computer-executable code. The operations further include extracting a set of features related to components of the source code data from the blocks of code. The extraction is performed by parsing the blocks of code using Abstract Syntax Tree (AST) data of the blocks of code. The operations further include revising the AST data. The operations further include updating the source code data based on the revised AST data and generating a dataset of NL and abstracted code features as training data based on the updated source code data and further training a language model on a sequence-to-sequence generation task.


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