Software update health regression detection

A system, comprising:

• a processing system;
• and
• memory storing instructions that, when executed, cause the system to perform operations comprising:
• identifying, from a group of computing nodes, a set of target nodes that have completed a deployment of a software update;
• obtaining first observation data of the set of target nodes;
• normalizing the first observation data into a plurality of time-based observation windows around the deployment of the software update;
• detecting a first regression condition associated with the software update by performing a first observational study comparing the normalized first observation data;
• identifying, from the group of computing nodes, a set of nodes that have not completed deployment of the software update;
• selecting, from the identified set of nodes that have not completed deployment of the software update, a set of control nodes that have a control factor in common with the set of target nodes;
• obtaining second observation data of the set of control nodes;
• detecting a second regression condition associated with the software update by performing a first observational study comparing the second observation data to at least a portion of the normalized first observation data;
• obtaining third observation data of the set of target nodes;
• detecting a third regression condition associated with the software update by performing an anomaly detection analysis comparing the third observation data with a baseline value;
• detecting a software regression associated with the software update when the first regression condition, the second regression condition, and the third regression condition match a set of high-confidence regression conditions;
• and
• in response to detecting the software regression, transmitting an event based on the software regression.

A system for performing a health analysis of a software update, comprising:

• a first observational study model that performs operations comprising:
• detecting a first regression condition associated with the software update, where the first regression condition is detected by performing a first observational study comparing first observation data collected from a set of target nodes of a group, where the set of target nodes has completed a deployment of the software update;
• a second observational study model that performs operations comprising:
• detecting a second regression condition associated with the software update by performing a second observational study comparing at least a portion of the first observation data to second observation data collected from a set of control nodes selected from the group;
• an anomaly detection model that performs operations comprising:
• detecting a third regression condition associated with the software update by performing an anomaly detection analysis comparing third observation data collected from the set of target nodes to a baseline value;
• a decision engine that performs operations comprising:
• detecting a software regression associated with the software update when the first regression condition, the second regression condition, and the third regression condition match a set of high-confidence regression conditions configured for the health analysis;
• and
• an escalation engine that performs operations comprising:
• in response to detecting the software regression, transmitting an event based on the software regression.