Code development using continued machine learnings

US10481879B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Joshua Bates Stevens · Issaquah, US
• John Tilford · Seattle, US
• Guillermo Serrato Castilla · Seattle, US
• Srivatsn Narayanan · Bothell, US
• Simon Calvert · Sammamish, US
• Mark Alistair WILSON-THOMAS · Mercer Island, US
• Deborah Chen · Seattle, US
• Miltiadis Allamanis · Cambridge, GB
• Marc Manuel Johannes Brockschmidt · Cambridge, GB
• Kesavan Shanmugam · Redmond, US

Key dates

• Filing date: Mar 30, 2018
• Grant date: Nov 19, 2019
• Expiry date: Mar 30, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N5/022
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Improving how a codebase, which may include source code, related databases, test files, code history, and/or changes, is drafted, edited, debugged, or otherwise developed. Machine learning is performed on a model codebase to establish a machine learning model. When a change to a codebase occurs, the machine learning model may be applied to evaluate that change. A change context providing context for this change is accessed. An analyzer then analyzes the change using the machine learning model and at least a part of the change context to generate an analysis result. Some information about the result is rendered. After rendering that information, a determination regarding how a user responded to the information is performed, and a subsequent analysis is then modified based on the user's response.