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.
Patent No.:
Date of Patent:
Dec. 30, 2025
Filed:
Sep. 13, 2023
Microsoft Technology Licensing, Llc, Redmond, WA (US);
Jose Antonio Hijar Miranda, Jalisco, MX;
Luke Robert Schoen, Woodinville, WA (US);
Mitansh Rakesh Shah, Seattle, WA (US);
Jorge Alejandro Velasco Reyna, Jalisco, MX;
Samuel Akwesi Yeboah, Houston, TX (US);
Sereym Baek, Marietta, GA (US);
Michael Joseph Laucella, Flushing, NY (US);
Everson Ramon Rodriguez Muniz, Bellevue, WA (US);
Ranjodh Singh Sandhu, St. Visalia, CA (US);
Florin Lazar, Woodinville, WA (US);
Robert Allen Land, Kirkland, WA (US);
Microsoft Technology Licensing, LLC, Redmond, WA (US);
Abstract
A machine learning model is trained from characteristics of code changes and characteristics of tests to generate an output indicative of a likely test result of running a corresponding test on a code change. One or more machine learning models may be trained for a specific code repository and based on developer feedback. When a code change is generated by a developer to code in a code repository, a machine learning model is selected based on the repository and characteristics or features of the code change are extracted and input to the machine learning model. The machine learning model generates a model output indicative of the likely test results of running each of a plurality of different tests on the code change. The model output indicates how likely it is that each of the plurality of different tests will fail. Based on the model output, a test selection system selects a subset of the plurality of different tests that should be run against the code changes.