correlating

Cross-incident correlation for root-cause analysis identifies recurring patterns across multiple postmortem reports to surface systemic vulnerabilities, persistent signal gaps, and common blast-radius trends. It processes PMR metadata to reveal root-cause trends and prevent repeat incidents.

To generate a cross-incident correlation report, input an array of PMR metadata objects representing incidents and regression data. The analysis outputs a structured markdown report containing hot spots, root-cause trends, persistent gaps, and fix-introduced patterns.

Yes, you can use incident metrics to find fix-introduced regression patterns by analyzing cross-PMR metadata. This reveals systemic vulnerabilities and communication gaps across time, ensuring that hot spots reference at least two PMRs to confirm recurrence.

The best way to analyze blast-radius patterns across postmortems is to correlate a set of PMR metadata objects. This surfaces hot spots and persistent gaps, generating a structured markdown report with a YAML-like schema for sprint planning and hardening.

Yes, cross-PMR correlation works for sprint planning and regression analysis by identifying persistent signal gaps and fix-introduced patterns. It processes incident metadata to reveal systemic vulnerabilities, helping close gaps before recurrence during development cycles.

PMR metadata inputs must be provided as an array of metadata objects representing incidents and regression data. All PMR IDs in the resulting correlation report must originate from this input, and detected hot spots must reference at least two PMRs.