Story cycle time anomaly prediction and root cause identification in an agile development environment
US10540573B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 6, 2018 |
| Grant date | Jan 21, 2020 |
| Priority date | — |
| Expiry date | Dec 6, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F11/0757
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server captures historical story data from an Agile development tracking system. For each completed story, the server generates a vector based upon story-specific features and assigns a label to the vector based upon a cycle time associated with the story. The server trains a classification model using a neural network on the vectors and labels. The server captures new story data from the Agile development tracking system. For each new story, the server generates a vector based upon story-specific features and executes the trained model on the vector to generate a cycle time prediction for the new story. Based upon the cycle time prediction, the server identifies deficiencies in the new story and generates an alert message.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.