Duplicate bug report detection using machine learning algorithms and automated feedback incorporation

US10339030B2 · kind B2 · utility

Assignee

• Oracle International Corporation · US

Inventors

• Prasad V. Bagal · San Jose, US
• Sameer Joshi · San Jose, US
• Hanlin Daniel Chien · San Jose, US
• Ricardo Rey Diez · Zapopan, MX
• David Woo · Zapopan, MX
• Emily Ronshien Su · Mountain View, US
• Sha Chang · Mountain View, US

Key dates

• Filing date: Jan 11, 2016
• Grant date: Jul 2, 2019
• Expiry date: Jan 27, 2036

Classification

• Technology area (CPC —): General

Abstract

Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.