evidence-based-planning

To write implementation plans with verifiable evidence, you must collect code and runtime verification before defining problems, validate assumptions through a Chain-of-Verification process, and audit citations to prevent incorrect references and invalid data assumptions.

Assumption verification in software planning is a structured Chain-of-Validation process that validates every assumption before coding. It prevents invalid designs by ensuring proposed code changes are directly linked to verified evidence of the problem they solve.

Historical regression analysis improves implementation planning by integrating git log data to learn from past failures. This evidence-first approach proactively identifies potential failure modes through pre-mortem analysis before finalizing your plan.

The best way to formalize implementation planning for refactoring is enforcing a strict phased approach from problem definition through validation. This mandates evidence collection, historical regression analysis, and assumption verification to minimize the risk of introducing new bugs.

Yes, evidence-based planning prevents runtime environment and data errors by mandating runtime verification before plan finalization. It enforces a strict phased approach from problem definition through validation to proactively identify potential failure modes.

Implementation plans reference non-existent code and invalid data assumptions when they lack strict citation audits and evidence collection. Applying a phased evidence-first approach with assumption verification ensures all plan elements are tied to verified evidence.