The opportunity
A legacy transaction-processing platform carries thousands of stored procedures. Migrating them is slow, error-prone, and ties up senior engineers for weeks at a time — which slows feature delivery and increases defect risk across the downstream product squads that depend on the platform.
This is the canonical Lab case: a real business problem, a reachable owner, and an outcome that can be measured before and after.
The hypothesis
A well-governed, AI-augmented SDLC can assist the riskiest, most repetitive parts of migration — specification, code, and tests — while keeping humans firmly in the loop. If it works, cycle time per procedure drops sharply with comparable or better quality, and senior engineering time is freed for higher-leverage work.
Current state → future state
| Current state | Legacy stored procedures slow feature delivery and increase defect risk; migrating them is manual, slow, and senior-heavy. |
| Problem / pain | Migration is slow, error-prone, and consumes weeks of scarce senior-engineer time. |
| Who is affected | Core Engineering, QA, and Platform teams; downstream product squads. |
| Impact focus | Time savings · risk reduction · quality. |
| What success looks like | A repeatable SDLC workflow where AI assists spec, code, and tests — lower cycle time, comparable or better quality. |
| What would improve | Reduce cycle time per procedure by 30–50%, lower defect escape, free up senior engineering time. |
| Constraints & risks | Must operate safely in non-production first; data governance for any training data. |
How impact is measured
The case defines its metrics up front — and reports against them after:
- PR cycle time — how long a change takes from open to merge.
- Migration throughput — procedures migrated per sprint.
- Defect escape rate — defects that slip past review into later stages.
Behavioral specs and test oracles anchor “comparable or better quality,” so speed is never bought at the cost of correctness.
Why it’s a Lab case
It is narrow, sponsored, and measurable — and if the pipeline proves out, it becomes a reusable capability: a governed, AI-augmented SDLC pattern other teams can adopt for their own legacy-migration work. Small win, designed to compound.
The exact system may be different. The pattern is what matters: a real workflow, a measurable pain, and a bounded first proof point.
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