Systems and methods to optimize testing using machine learning
US11256609B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 29, 2021 |
| Grant date | Feb 22, 2022 |
| Priority date | — |
| Expiry date | Jul 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/006
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine learning (ML) model is created via training or re-training one or more ML algorithms using past release(s) data (e.g., data comprising of requirements and corresponding test cases). The ML model comprises various clusters and these clusters are dynamically created every time when the ML model is trained (or retrained). One or more requirements exist in each cluster, and each requirement has one or more test cases associated with it. New requirements are mapped to a particular cluster and then test cases are compared against a universe of other test cases to determine whether to add a particular test case to a list of test cases that test the new requirement.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.