Microsoft technology licensing, llc (20250086095). MACHINE LEARNING-BASED TEST SELECTION FOR AUTOMATED CODE VALIDATION

MACHINE LEARNING-BASED TEST SELECTION FOR AUTOMATED CODE VALIDATION

Organization Name

microsoft technology licensing, llc

Inventor(s)

Jose Antonio Hijar Miranda of Jalisco (MX)

Luke Robert Schoen of Woodinville WA (US)

Mitansh Rakesh Shah of Seattle WA (US)

Jorge Alejandro Velasco Reyna of Zapopan (MX)

Samuel Akwesi Yeboah of Houston TX (US)

Sereym Baek of Marietta GA (US)

Michael Joseph Laucella of Flushing NY (US)

Everson Ramon Rodriguez Muniz of Bellevue WA (US)

Ranjodh Singh Sandhu of St. Visalia CA (US)

Florin Lazar of Woodinville WA (US)

Robert Allen Land of Kirkland WA (US)

MACHINE LEARNING-BASED TEST SELECTION FOR AUTOMATED CODE VALIDATION

This abstract first appeared for US patent application 20250086095 titled 'MACHINE LEARNING-BASED TEST SELECTION FOR AUTOMATED CODE VALIDATION

Original Abstract Submitted

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.