Automated detection of code regressions from time-series data
US11720461B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 12, 2019 |
| Grant date | Aug 8, 2023 |
| Priority date | — |
| Expiry date | Jan 7, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In non-limiting examples of the present disclosure, systems, methods and devices for detecting and classifying service issues associated with a cloud-based service are presented. Operational event data for a plurality of operations associated with the cloud-based application service may be monitored. A statistical-based unsupervised machine learning model may be applied to the operational event data. A subset of the operational event data may be tagged as potentially being associated with a code regression, wherein the subset comprises a time series of operational event data. A neural network may be applied to the time series of operational event data, and the time series of operational event data may be flagged for follow-up if the neural network classifies the time series as relating to a positive code regression category.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.