Retrospective Validation

Retrospective validation automates assessment of methodology effectiveness by applying it to historical data (100+ instances) and measuring prevented errors, test coverage, and ROI. The four-phase workflow collects data, defines patterns using regex or signatures, executes validation, and computes confidence scores without requiring live deployment.

Use retrospective validation when live deployment is costly or risky but you have rich historical data available. It's ideal for evaluating error recovery strategies, test approaches, and performance optimizations by analyzing past instances to predict future methodology impact.

Retrospective validation requires historical data with 100+ instances and works with Read, Grep, Glob, and Bash tools to collect and analyze patterns. You define detection rules using regex or signatures against this historical dataset to assess methodology effectiveness.

The validation generates coverage, prevention, ROI, and confidence metrics. Coverage shows what the methodology detects, prevention quantifies errors avoided, ROI estimates business value, and confidence reflects the reliability of the assessment based on historical pattern matching.

Yes. Retrospective validation is designed to process existing error histories and incident records using pattern-based detection. It transforms this historical data into structured insights about methodology effectiveness without requiring changes to live systems.

The rule-based detection engine uses regex patterns and signatures to identify errors and events in historical data. These patterns are applied across 100+ instances to measure how a given methodology would have prevented, detected, or recovered from past failures.