Assumptions hide inside familiar systems
Long-lived applications feel understood because people use them every day. Yet key behavior may be distributed across old code, stored procedures, spreadsheet steps, scheduled jobs, report expressions, and operational judgment.
A modernization plan based on interviews alone will miss edge cases and contradictions. Evidence ties explanations to actual inputs, paths, data, and outputs.
Create a behavioral baseline
A baseline does not mean preserving every defect. It means recording the current behavior well enough to decide intentionally what must match, what should change, and what cannot be trusted.
Useful evidence includes representative scenarios, known-good outputs, dependency inventories, data profiles, calculation examples, and recovery procedures.
- Representative and boundary cases
- Authoritative reports and exports
- Business-rule and dependency maps
- Known defects and unverifiable history
Use discrepancies to learn
When the old and new systems disagree, the difference is information. It may expose a new defect, a legacy exception, a source-data problem, or an incomplete requirement.
A disciplined team records the discrepancy, determines the cause, and obtains an explicit decision. It does not quietly adjust the baseline until a test turns green.
Make acceptance reproducible
Stakeholders should be able to review why a phase is ready: which scenarios passed, which intentional changes were approved, which risks remain, and what rollback or monitoring protects operations.
That evidence improves technical quality, but it also improves governance. Leaders can make scope and sequencing decisions with a clear view of what is known.
- Traceable acceptance criteria
- Automated regression and parity tests
- Documented exceptions
- Phased operational handoff