Forecasting a quality of a software release using machine learning
US11347629B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 31, 2018 |
| Grant date | May 31, 2022 |
| Priority date | — |
| Expiry date | Oct 31, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/06312
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In some examples, a server may retrieve and parse test results associated with testing a software package. The server may determine a weighted sum of a software feature index associated with a quality of the plurality of features, a defect index associated with the defects identified by the test cases, a test coverage index indicating a pass rate of the plurality of test cases, a release release reliability index associated with results of executing regression test cases included in the test cases, and an operational quality index associated with resources and an environment associated with the software package. The server may use a machine learning algorithm, such as a time series forecasting algorithm, to forecast a release status of the software package. The server may determine, based on the release status, whether the software package is to progress from a current phase to a next phase of a development cycle.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.