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:
Aug. 08, 2023

Filed:

Jul. 12, 2018
Applicant:

Microsoft Technology Licensing, Llc., Redmond, WA (US);

Inventors:

Anshul Gupta, Kirkland, WA (US);

Neelakantan Sundaresan, Bellevue, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/025 (2023.01); G06F 8/35 (2018.01); G06F 8/73 (2018.01); G06F 11/36 (2006.01); G06N 5/04 (2023.01); G06N 3/045 (2023.01); G06N 7/01 (2023.01); G06F 8/30 (2018.01); G06F 8/41 (2018.01); G06F 8/71 (2018.01); G06F 8/75 (2018.01); G06N 3/08 (2023.01); G06N 3/084 (2023.01); G06N 3/044 (2023.01);
U.S. Cl.
CPC ...
G06N 5/025 (2013.01); G06F 8/30 (2013.01); G06F 8/35 (2013.01); G06F 8/42 (2013.01); G06F 8/71 (2013.01); G06F 8/73 (2013.01); G06F 8/75 (2013.01); G06F 11/3604 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06N 7/01 (2023.01); G06F 11/3616 (2013.01); G06N 3/044 (2023.01); G06N 3/084 (2013.01);
Abstract

A code review process utilizes a deep learning model trained on historical code reviews to automatically perform peer or code review of a source code file. The deep learning model is able to predict the code reviews relevant to a source code snippet by learning from historical code reviews. The deep learning model is trained on pairs of code snippets and code reviews that are relevant to each other and pairs of code snippets and code reviews that have no relation to each other. The deep learning model is data driven thereby not relying on pre-configured rules which makes the model adaptable to different review environments.


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