US Patent Application 18338166. AUTOMATED DETECTION OF CODE REGRESSIONS FROM TIME-SERIES DATA simplified abstract
Contents
AUTOMATED DETECTION OF CODE REGRESSIONS FROM TIME-SERIES DATA
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Rahul Nigam of Bothell WA (US)
Andrei Nicolae of Bellevue WA (US)
Mark Raymond Gilbert of Issaquah WA (US)
Vinod Mukundan Menon of Bothell WA (US)
AUTOMATED DETECTION OF CODE REGRESSIONS FROM TIME-SERIES DATA - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 18338166 Titled 'AUTOMATED DETECTION OF CODE REGRESSIONS FROM TIME-SERIES DATA'
Simplified Explanation
This abstract describes systems, methods, and devices for detecting and categorizing service issues in a cloud-based service. It involves monitoring operational event data related to the service and applying a statistical-based unsupervised machine learning model to analyze the data. A subset of the data is identified as potentially indicating a code regression, and a neural network is used to further analyze the time series of this subset. If the neural network classifies the time series as a positive code regression category, it is flagged for further investigation.
Original Abstract Submitted
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.
