Health indicator platform for software regression reduction

A method for automatically reducing software regression for a software payload applied to a plurality of computing platforms by a software updater, the method comprising:

• receiving, from a software updater, a health request associated with the software payload;
• in response to receiving the health request, retrieving, from an escalation engine, a plurality of identifiers identifying a subset of the plurality of computing platforms that have completed deployment of the software payload
• determining a plurality of unified logging service (ULS) tags associated with the software payload;
• querying an anomaly detector for failure data, for the subset of the plurality of computing platforms, corresponding to the ULS tags, the failure data including pre-deployment failure data and post-deployment failure data;
• detecting a potential software regression associated with the software payload by comparing the pre-deployment failure data and the post-deployment failure data;
• in response to detecting a potential software regression, querying a root cause analyzer based on the potential software regression;
• receiving, from the root cause analyzer, at least one change list identifier identifying at least one potential root cause for the potential software regression;
• and
• transmitting, to an escalation engine, an event based on the potential software regression and the at least one potential root cause.

A non-transitory computer-readable medium including instructions executable by an electronic processor to perform a set of functions, the set of functions comprising:

• receiving, from a software updater, a health request associated with the software payload;
• in response to receiving the health request, retrieving, from an escalation engine, a plurality of identifiers identifying a subset of the plurality of computing platforms that have completed deployment of the software payload
• determining a plurality of unified logging service (ULS) tags associated with the software payload;
• querying an anomaly detector for failure data, for the subset of the plurality of computing platforms, corresponding to the ULS tags, the failure data including pre-deployment failure data and post-deployment failure data;
• comparing the pre-deployment failure data and the post-deployment failure data to determine whether a potential software regression associated with the software payload is detected;
• when a potential software regression is detected, querying a root cause analyzer based on the potential software regression;
• receiving, from the root cause analyzer, at least one change list identifier identifying at least one potential root cause for the potential software regression;
• and
• transmitting, to an escalation engine, an event based on the potential software regression and the at least one potential root cause.